Public surveillance Systems are creating ground-breaking impact to secure lives, ensuring public safety with the interventions that will curb crime and improve well-being of communities, law enforcement agencies are employing and deploying tools like CCTV for video surveillance in banks, residential societies shopping malls,markets,roads almost everywhere to detect where and who was responsible for events like robbery, rough driving, insolence, murder etc. Other surveillance system tools like phone tapping is used to plot an event to catch a suspected person who is threatening an innocent or conspiring a violent activity over a phone call which is being done voluntarily by authorities . Now analyzing both the scenarios in the case of CCTV the crime(robbery,murder) has already occured and in the case of phone tapping there must be information in advance about the suspected person who is going to commit a crime, Now to overcome this issue a system is proposed to know in advance who is suspected and what conspiracy is being done over phone calls as well to detect it automatically and report it to law enforcemet authorities.
NETWORK INTRUSION DATASETS USED IN NETWORK SECURITY EDUCATIONIJITE
There is a gap between the network security graduate and the professional life. In this paper we discussed the different types of network intrusion dataset and then we highlighted the fact that any student can easily create a network intrusion dataset that is representative of the network they are in. Intrusions can be in form of anomaly or network signature; the students cannot grasp all types but they have to have the ability to detect malicious packets within his network.
Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing,
and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with
detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes
defending against attacks hard and can easily lead to disclosure of sensitive information.
In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the
performance of an unsupervised learning method for anomaly detection. The results of the evaluation
showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper
presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by
using TreeView visualization tool .
AN ONTOLOGY FOR EXPLORING KNOWLEDGE IN COMPUTER NETWORKSijcsa
Ontology is applied to impart knowledge in various fields of Information Technology made it more intelligent in the past few decades. Many Ontologies was built on various domains like biology, medicine,physics, chemistry, and mathematics. The Ontology in computer science domain are limited and even the Ontology is not explored in detail. The knowledge in the field of computer networks is very large, which makes it more difficult for a human to expertise in. This paper proposes the Ontology in computer network domain on various perspectives like scope, scale, topology, communication media, OSI model, TCP/ IP model, protocol, security, network operating system, network hardware and performance. The Ontology is developed in OWL format, which can be easily integrated with any other semantic based applications. The network Ontology can be employed in Semantic Web applications to help the users to search for concepts computer networks domain
An Approach for Securing Voice Communication via Image Watermarking TechniqueCSCJournals
Nowadays, Internet users use many social websites for communication with each other, and some of the users may live in another part of the world. Thus, communication becomes much easier and faster. However, with internet growing and their users, it becomes more likely to be attacked by hackers and intruders to access people personal data. Also, some professional hackers can detect the entire communication component for instance; text, image, video and voice messages.
In this paper, it is focused on one component, which is voice communication; and the main idea of this work is to hide the voice message that generated from one side and convert to a wave data, then to the binary code consequently on the receiver side. Later, the generated code will hide inside an image before reaching the destination. The final step is to reverse the whole security process that has been done in the source side. Therefore it can provide a safe environment for voice communication among the internet client.
A Hybrid Method of Long Short-Term Memory and AutoEncoder Architectures for S...AhmedAdilNafea
Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being tackled, the performance drops. This is because averaging the embedding of each term in a sentence to get the general embedding would discard the important embedding of some terms. LSTM showed significant improvement in terms of document embedding. However, within the classification LSTM requires adding additional information in order to precisely classify the document into sarcasm or not. This study aims to propose two technique based on LSTM and Auto-Encoder for improving the sarcasm detection. A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied. These include stop word removal, tokenization and special character removal with LSTM which can be represented by configuring the document embedding and using Auto-Encoder the classifier that was trained on the proposed LSTM. Results showed that the proposed LSTM with Auto-Encoder outperformed the baseline by achieving 84% of f-measure for the dataset. The main reason behind the superiority is that the proposed auto encoder is processing the document embedding as input and attempt to output the same embedding vector. This will enable the architecture to learn the interesting embedding that have significant impact on sarcasm polarity.
NETWORK INTRUSION DATASETS USED IN NETWORK SECURITY EDUCATIONIJITE
There is a gap between the network security graduate and the professional life. In this paper we discussed the different types of network intrusion dataset and then we highlighted the fact that any student can easily create a network intrusion dataset that is representative of the network they are in. Intrusions can be in form of anomaly or network signature; the students cannot grasp all types but they have to have the ability to detect malicious packets within his network.
Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing,
and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with
detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes
defending against attacks hard and can easily lead to disclosure of sensitive information.
In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the
performance of an unsupervised learning method for anomaly detection. The results of the evaluation
showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper
presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by
using TreeView visualization tool .
AN ONTOLOGY FOR EXPLORING KNOWLEDGE IN COMPUTER NETWORKSijcsa
Ontology is applied to impart knowledge in various fields of Information Technology made it more intelligent in the past few decades. Many Ontologies was built on various domains like biology, medicine,physics, chemistry, and mathematics. The Ontology in computer science domain are limited and even the Ontology is not explored in detail. The knowledge in the field of computer networks is very large, which makes it more difficult for a human to expertise in. This paper proposes the Ontology in computer network domain on various perspectives like scope, scale, topology, communication media, OSI model, TCP/ IP model, protocol, security, network operating system, network hardware and performance. The Ontology is developed in OWL format, which can be easily integrated with any other semantic based applications. The network Ontology can be employed in Semantic Web applications to help the users to search for concepts computer networks domain
An Approach for Securing Voice Communication via Image Watermarking TechniqueCSCJournals
Nowadays, Internet users use many social websites for communication with each other, and some of the users may live in another part of the world. Thus, communication becomes much easier and faster. However, with internet growing and their users, it becomes more likely to be attacked by hackers and intruders to access people personal data. Also, some professional hackers can detect the entire communication component for instance; text, image, video and voice messages.
In this paper, it is focused on one component, which is voice communication; and the main idea of this work is to hide the voice message that generated from one side and convert to a wave data, then to the binary code consequently on the receiver side. Later, the generated code will hide inside an image before reaching the destination. The final step is to reverse the whole security process that has been done in the source side. Therefore it can provide a safe environment for voice communication among the internet client.
A Hybrid Method of Long Short-Term Memory and AutoEncoder Architectures for S...AhmedAdilNafea
Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level analysis. However, once a sequence of terms is being tackled, the performance drops. This is because averaging the embedding of each term in a sentence to get the general embedding would discard the important embedding of some terms. LSTM showed significant improvement in terms of document embedding. However, within the classification LSTM requires adding additional information in order to precisely classify the document into sarcasm or not. This study aims to propose two technique based on LSTM and Auto-Encoder for improving the sarcasm detection. A benchmark dataset has been used in the experiments along with several pre-processing operations that have been applied. These include stop word removal, tokenization and special character removal with LSTM which can be represented by configuring the document embedding and using Auto-Encoder the classifier that was trained on the proposed LSTM. Results showed that the proposed LSTM with Auto-Encoder outperformed the baseline by achieving 84% of f-measure for the dataset. The main reason behind the superiority is that the proposed auto encoder is processing the document embedding as input and attempt to output the same embedding vector. This will enable the architecture to learn the interesting embedding that have significant impact on sarcasm polarity.
AN EMPIRICAL ANALYSIS OF EMAIL FORENSICS TOOLSIJNSA Journal
Emails are the most common service on the Internet for communication and sending documents. Email is used not only from computers but also from many other electronic devices such as tablets; smartphones, etc. Emails can also be used for criminal activities. Email forensic refers to the study of email detail and content as evidence to identify the actual sender and recipient of a message, date/time of transmission, detailed record of email transaction, intent of the sender, etc. Email forensics involves investigation of metadata, keyword, searching, port scanning and generating report based on investigators need. Many tools are available for any investigation that involves email forensics. Investigators should be very careful of not violating user’s privacy. To this end, investigators should run keyword searches to reveal only the relevant emails. Therefore, knowledge of the features of the tool and the search features is necessary for the tool selection. In this research, we experimentally compare the performance of several email forensics tools. Our aim is to help the investigators with the tool selection task. We evaluate the tools in terms of their keyword search, report generation, and other features such as, email format, size of the file accepted, whether they work online or offline, format of the reports, etc. We use Enron email dataset for our experiment.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Intrusion Detection System Using Self Organizing Map AlgorithmsEditor IJCATR
With the rapid expansion of computer usage and computer network the security of the computer system has became very
im
portant. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of
intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based o
n neur
al
networks that are suitable for Intrusion Detection Systems (IDS)
.
The name of this
algorithm is "Self Organizing Maps" (SOM).
So
far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the
methods
used to detect attacks in intrusion detection is done, In this paper we investigate the
Self
-
Organizing
Map
method.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...ijaia
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text-dependent speaker identification system. It contains three main modules: endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short-Term Energy analysis is used for endpoint detection. Mel-frequency Cepstrum Coefficients (MFCC) is applied for feature extraction to extract a small amount of data from the voice signal that can later be used to represent each speaker. For feature matching, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm is proposed because it can reduce the amount of data and complexity. The experimental study shows that the proposed system is more robust than using the original system and it is faster in computation than the existing one. To implement this system MATLAB is used for programming. Zaw Win Aung"A Robust Speaker Identification System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18274.pdf http://www.ijtsrd.com/other-scientific-research-area/other/18274/a-robust-speaker-identification-system/zaw-win-aung
As the enormous use of internet increases day by day so as security concern is also raise day by day over
the internet. In this paper we discuss the network security and its related threats and also study the types of
protocols and few issues related to protocols in computer networks. We also simulate the design of 5 node
wired network scenario, its packet drop rate analysis through TCP protocol using NS2 as a simulator.
Analyzed the performance of 5-node network when the packet is drop down by graphical method also
called as Xgraph when rate parameter is in mb and also analyzed the performance of same network by
changing the value of rate parameter at same time so no packets would drop down at same time and also
analyzed the performance by Xgraph method.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
Online interview is not a new thing but in this covid-19 situation it seems to be the only option. However, assessing the candidate on a video call may not be that effective. Having an AI based Interview Assessment System could prove to be useful, which would take input as speech and will give output as detailed analysis of that speech. While most the research work currently done focuses only on finding sentiment or personality from speech, our system aims to extract multiple information from the speech and provide a detailed analysis. The analysis would include a detailed report containing results about confidence level of the person, his/her emotional state, speed of the speech, frequently repeated words and also personality reflected by that speech. An interview panel consists of various members focusing on different aspect of the answer given by the candidate, some focus on technical correctness while, some simply want to check the communication skills of the candidate. Having an AI system giving a report on the soft skills part would reduce the work for interviewer and he/she could give complete focus on the technical correctness of the answer. This could eventually help save time and resources used by organizations for hiring process. This intention of creating this system is to assist the interview process and give analysis report based on the speech input instead a giving a verdict about selection of the candidate. Thus, this system could use not only by the interviewers but also by the candidates. The output provided would be a detailed report which could prove to be a good feedback for the students who are preparing for the interview. Having a feedback would help candidates work on their week points and thus perform better in further interviews.
IJWMN -Malware Detection in IoT Systems using Machine Learning Techniquesijwmn
Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.
MALWARE DETECTION IN IOT SYSTEMS USING MACHINE LEARNING TECHNIQUESijwmn
Malware detection in IoT environments necessitates robust methodologies. This study introduces
a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against
established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5%
accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model
construction, and the LSTM classifier exhibited heightened accuracy in classification.
Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed
model, highlighting its potential for enhancing IoT security. The study advocates for future
exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and
underscores the importance of predictive analyses for a more powerful IOT security. This
research serves as a platform for developing more resilient security measures in IoT ecosystems.
Review on effectiveness of deep learning approach in digital forensicsIJECEIAES
Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.
Touch is one of the most common forms of sign language used in oral communication. It is most commonly used by deaf and dumb people who have difficulty hearing or speaking. Communication between them or ordinary people. Various sign-language programs have been developed by many manufacturers around the world, but they are relatively flexible and affordable for end users. Therefore, this paper has presented software that introduces a type of system that can automatically detect sign language to help deaf and mute people communicate better with other people or ordinary people. Pattern recognition and hand recognition are developing fields of research. Being an integral part of meaningless hand-to-hand communication plays a major role in our daily lives. The handwriting system gives us a new, natural, easy-to-use communication system with a computer that is very common to humans. Considering the similarity of the human condition with four fingers and one thumb, the software aims to introduce a real-time hand recognition system based on the acquisition of some of the structural features such as position, mass centroid, finger position, thumb instead of raised or folded finger.
AN EMPIRICAL ANALYSIS OF EMAIL FORENSICS TOOLSIJNSA Journal
Emails are the most common service on the Internet for communication and sending documents. Email is used not only from computers but also from many other electronic devices such as tablets; smartphones, etc. Emails can also be used for criminal activities. Email forensic refers to the study of email detail and content as evidence to identify the actual sender and recipient of a message, date/time of transmission, detailed record of email transaction, intent of the sender, etc. Email forensics involves investigation of metadata, keyword, searching, port scanning and generating report based on investigators need. Many tools are available for any investigation that involves email forensics. Investigators should be very careful of not violating user’s privacy. To this end, investigators should run keyword searches to reveal only the relevant emails. Therefore, knowledge of the features of the tool and the search features is necessary for the tool selection. In this research, we experimentally compare the performance of several email forensics tools. Our aim is to help the investigators with the tool selection task. We evaluate the tools in terms of their keyword search, report generation, and other features such as, email format, size of the file accepted, whether they work online or offline, format of the reports, etc. We use Enron email dataset for our experiment.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
Intrusion Detection System Using Self Organizing Map AlgorithmsEditor IJCATR
With the rapid expansion of computer usage and computer network the security of the computer system has became very
im
portant. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of
intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based o
n neur
al
networks that are suitable for Intrusion Detection Systems (IDS)
.
The name of this
algorithm is "Self Organizing Maps" (SOM).
So
far, many different methods have been used to build a detector that Wide variety of different ways in the covers. Among the
methods
used to detect attacks in intrusion detection is done, In this paper we investigate the
Self
-
Organizing
Map
method.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...ijaia
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text-dependent speaker identification system. It contains three main modules: endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short-Term Energy analysis is used for endpoint detection. Mel-frequency Cepstrum Coefficients (MFCC) is applied for feature extraction to extract a small amount of data from the voice signal that can later be used to represent each speaker. For feature matching, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm is proposed because it can reduce the amount of data and complexity. The experimental study shows that the proposed system is more robust than using the original system and it is faster in computation than the existing one. To implement this system MATLAB is used for programming. Zaw Win Aung"A Robust Speaker Identification System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18274.pdf http://www.ijtsrd.com/other-scientific-research-area/other/18274/a-robust-speaker-identification-system/zaw-win-aung
As the enormous use of internet increases day by day so as security concern is also raise day by day over
the internet. In this paper we discuss the network security and its related threats and also study the types of
protocols and few issues related to protocols in computer networks. We also simulate the design of 5 node
wired network scenario, its packet drop rate analysis through TCP protocol using NS2 as a simulator.
Analyzed the performance of 5-node network when the packet is drop down by graphical method also
called as Xgraph when rate parameter is in mb and also analyzed the performance of same network by
changing the value of rate parameter at same time so no packets would drop down at same time and also
analyzed the performance by Xgraph method.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
Online interview is not a new thing but in this covid-19 situation it seems to be the only option. However, assessing the candidate on a video call may not be that effective. Having an AI based Interview Assessment System could prove to be useful, which would take input as speech and will give output as detailed analysis of that speech. While most the research work currently done focuses only on finding sentiment or personality from speech, our system aims to extract multiple information from the speech and provide a detailed analysis. The analysis would include a detailed report containing results about confidence level of the person, his/her emotional state, speed of the speech, frequently repeated words and also personality reflected by that speech. An interview panel consists of various members focusing on different aspect of the answer given by the candidate, some focus on technical correctness while, some simply want to check the communication skills of the candidate. Having an AI system giving a report on the soft skills part would reduce the work for interviewer and he/she could give complete focus on the technical correctness of the answer. This could eventually help save time and resources used by organizations for hiring process. This intention of creating this system is to assist the interview process and give analysis report based on the speech input instead a giving a verdict about selection of the candidate. Thus, this system could use not only by the interviewers but also by the candidates. The output provided would be a detailed report which could prove to be a good feedback for the students who are preparing for the interview. Having a feedback would help candidates work on their week points and thus perform better in further interviews.
IJWMN -Malware Detection in IoT Systems using Machine Learning Techniquesijwmn
Malware detection in IoT environments necessitates robust methodologies. This study introduces a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5% accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model construction, and the LSTM classifier exhibited heightened accuracy in classification. Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed model, highlighting its potential for enhancing IoT security. The study advocates for future exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and underscores the importance of predictive analyses for a more powerful IOT security. This research serves as a platform for developing more resilient security measures in IoT ecosystems.
MALWARE DETECTION IN IOT SYSTEMS USING MACHINE LEARNING TECHNIQUESijwmn
Malware detection in IoT environments necessitates robust methodologies. This study introduces
a CNN-LSTM hybrid model for IoT malware identification and evaluates its performance against
established methods. Leveraging K-fold cross-validation, the proposed approach achieved 95.5%
accuracy, surpassing existing methods. The CNN algorithm enabled superior learning model
construction, and the LSTM classifier exhibited heightened accuracy in classification.
Comparative analysis against prevalent techniques demonstrated the efficacy of the proposed
model, highlighting its potential for enhancing IoT security. The study advocates for future
exploration of SVMs as alternatives, emphasizes the need for distributed detection strategies, and
underscores the importance of predictive analyses for a more powerful IOT security. This
research serves as a platform for developing more resilient security measures in IoT ecosystems.
Review on effectiveness of deep learning approach in digital forensicsIJECEIAES
Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.
Touch is one of the most common forms of sign language used in oral communication. It is most commonly used by deaf and dumb people who have difficulty hearing or speaking. Communication between them or ordinary people. Various sign-language programs have been developed by many manufacturers around the world, but they are relatively flexible and affordable for end users. Therefore, this paper has presented software that introduces a type of system that can automatically detect sign language to help deaf and mute people communicate better with other people or ordinary people. Pattern recognition and hand recognition are developing fields of research. Being an integral part of meaningless hand-to-hand communication plays a major role in our daily lives. The handwriting system gives us a new, natural, easy-to-use communication system with a computer that is very common to humans. Considering the similarity of the human condition with four fingers and one thumb, the software aims to introduce a real-time hand recognition system based on the acquisition of some of the structural features such as position, mass centroid, finger position, thumb instead of raised or folded finger.
PREPROCESSING CHALLENGES FOR REAL WORLD AFFECT RECOGNITIONCSEIJJournal
Real world human affect recognition requires immediate attention which is a significant aspect of humancomputer interaction. Audio-visual modalities can make a significant contribution by providing rich
contextual information. Preprocessing is an important step in which the relevant information is extracted.
It has a crucial impact on prominent feature extraction and further processing. The main aim is to
highlight the challenges in preprocessing real world data. The research focuses on experimental testing
and comparative analysis for preprocessing using OpenCV, Single Shot MultiBox Detector (SSD), DLib,
Multi-Task Cascaded Convolutional Neural Networks (MTCNN), and RetinaFace detectors. The
comparative analysis shows that MTCNN and RetinaFace give better performance in real world data. The
performance of facial affect recognition using a pre-trained CNN model is analysed with a lab-controlled
dataset CK+ and a representative wild dataset AFEW. This comparative analysis demonstrates the impact
of preprocessing issues on feature engineering framework in real world affect recognition.
Preprocessing Challenges for Real World Affect Recognition CSEIJJournal
Real world human affect recognition requires immediate attention which is a significant aspect of human-
computer interaction. Audio-visual modalities can make a significant contribution by providing rich
contextual information. Preprocessing is an important step in which the relevant information is extracted.
It has a crucial impact on prominent feature extraction and further processing. The main aim is to
highlight the challenges in preprocessing real world data. The research focuses on experimental testing
and comparative analysis for preprocessing using OpenCV, Single Shot MultiBox Detector (SSD), DLib,
Multi-Task Cascaded Convolutional Neural Networks (MTCNN), and RetinaFace detectors. The
comparative analysis shows that MTCNN and RetinaFace give better performance in real world data. The
performance of facial affect recognition using a pre-trained CNN model is analysed with a lab-controlled
dataset CK+ and a representative wild dataset AFEW. This comparative analysis demonstrates the impact
of preprocessing issues on feature engineering framework in real world affect recognition.
Abstract: The main communication methods used by deaf people are sign language, but opposed to common thought, there is no specific universal sign language: every country or even regional group uses its own set of signs. The use of sign language in digital systems can enhance communication in both directions: animated avatars can synthesize signals based on voice or text recognition; and sign language can be translated into various text or sound forms based on different images, videos and sensors input. The ultimate goal of this research, but it is not a simple spelling of spoken language, so that recognizing different signs or letters of the alphabet (which has been a common approach) is not sufficient for its transcription and automatic interpretation. Here proposes an algorithm and method for an application this would help us in recognising the various user defined signs. The palm images of right and left hand are loaded at runtime. Firstly these images will be seized and stored in directory. Then technique called Template matching is used for finding areas of an image that match (are similar) to a template image (patch). Our goal is to detect the highest matching area. We need two primary components- A) Source image (I): In the template image in which we try to find a match. B) Template image (T): The patch image which will be compared to the template image. In proposed system user defined patterns will be having 60% accuracy while default patterns will be provided with 80% accuracy.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimizatio...IJECEIAES
The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.
Hints of the outbreak are detected through the modified circumstances favoring the outbreaks, like the warm weather contributing to epidermal outbreaks or the loss of sanitation leading to cholera outbreaks typically relying on the routine reports from the healthcare facilities, secondary data like attendance monitoring at workplaces and schools, the web, and the media play a significant informational source with more than 60% of the initial outbreak reporting to the informal sources. Through the application of natural language processing methods and machine learning technologies, a pipeline is developed which extracts the critical entities like country, confirmed case counts, disease, and case dates, which are mandatory entities from the epidemiological article and are saved in the database thereby facilitating the data entry easier. The advantages are the facilitation of relevant score articles shown first, thereby providing the web service results termed EventEpi integrated into the Event Based Surveillance (EBS) workflows.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
High Accuracy Location Information Extraction From Social Network Texts Using...kevig
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
A Survey on Speech Recognition with Language Specificationijtsrd
As a cross disciplinary, speech recognition is entirely based on the speech as the survey object. Speech recognition allows the machine to convert the speech signal into text or commands via the process of identification and understanding. Speech recognition involves in various fields of physiology, psychology, linguistics, computer science and signal processing, and is even related to the person’s body language, and its goal is to achieve natural language communication between man and machine. The speech recognition technology is gradually becoming the key technology of the IT man machine interface. This paper describes the development of speech recognition technology and its basic principles, methods, reviewed the classification of speech recognition systems, speech recognition approaches and voice recognition technology, analyzed the problems faced by the speech recognition. Dr. Preeti Savant | Lakshmi Sandhya H "A Survey on Speech Recognition with Language Specification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49370.pdf Paper URL: https://www.ijtsrd.com/computer-science/speech-recognition/49370/a-survey-on-speech-recognition-with-language-specification/dr-preeti-savant
https://jst.org.in/index.html
Our journal has digital transformation, effective management strategies are crucial. Our pages unfold discussions on navigating the complexities of modern business landscapes, strategic decision-making, and adaptive leadership—essential elements for success in the 21st century.
New kind of intrusions causes deviation in the normal behaviour of traffic flow in
computer networks every day. This study focused on enhancing the learning capabilities of IDS
to detect the anomalies present in a network traffic flow by comparing the k-means approach of
data mining for intrusion detection and the outlier detection approach. The k-means approach
uses clustering mechanisms to group the traffic flow data into normal and abnormal clusters.
Outlier detection calculates an outlier score (neighbourhood outlier factor (NOF)) for each flow
record, whose value decides whether a traffic flow is normal or abnormal. These two methods
were then compared in terms of various performance metrics and the amount of computer
resources consumed by them. Overall, k-means was more accurate and precise and has better
classification rate than outlier detection in intrusion detection using traffic flows. This will help
systems administrators in their choice of IDS.
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This paper is explaining that how only leadership is responsible for sustainable improvement and
growth and only it can lead the organization towards improvement and overall development. Leadership and its
effectiveness are discussed in this research work and also how leadership is a different way of the success of the
organization and different from the traditional management to create true work-culture and good-will of the
organization in the social scene. Leadership is only responsible in bringing positive and negative change in the
organization; if the leadership doesn’t have the concern in the organization, the organization will not be able to
lead in the right direction towards improvement and development.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
n today’s society, we stand before a change in energy scarcity. As our civilization grows, many
countries in thedeveloping world seek to have the standard of living that has been exclusive to a few nations, so
their arises a need in thedevelopment of technology that is compatible enough with the resources provided by
nature in order to have sustainabledevelopment to all class of the society. In order to overcomethe prevailing
challenges of huge energy crises in near future, there is an urgent need for the development of electrical
vehiclesor hybrid electrical vehicles with low CO2 emissions using renewable energy sources. In view of the
above, electrochemicalcapacitors can fulfil the requirements to some extent.Preparation of nano composite
polymer gel electrolyte is the best optional product to overcome these problems. When fillers are added or
dispersed to the polymer gel electrolyte, amorphous or porous nature of electrolyte increases which enhances
the liquid absorbing quality of polymer and helps in removing the drawbacks of polymer gel electrolytes such as
leakage, poor mechanical and thermal stability etc. In this work dispersion of SiO2 nano filler is done in the
[PVdF (HFP)-PC-Mg (ClO4)2] for the synthesis of nano composite PGE [PVdF (HFP)-PC-MgClO4- SiO2].
Optimization and characterization was carried out by using various techniques.
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Top 10 Oil and Gas Projects in Saudi Arabia 2024.pdf
A Deep Learning Model For Crime Surveillance In Phone Calls.
1. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
1
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A Deep Learning Model For Crime Surveillance In Phone Calls.
Abhishek Khaire1
, Pranav Mahadeokar2
, Praveen Kumar Prajapati3
, Prof.
Sunita Naik4
1
(Department of Computer Engineering, Mumbai University, India)
Email: 18301062abhishek@viva-technology.org
2
(Department of Computer Engineering, Mumbai University, India)
Email: 18312069pranav@viva-technology.org
3
(Department of Computer Engineering, Mumbai University, India)
Email: 18301072praveenkumar@viva-technology.org
4
(Department of Computer Engineering, Mumbai University, India)
Email: sunitanaik@viva-technology.org
Abstract: Public surveillance Systems are creating ground-breaking impact to secure lives, ensuring public
safety with the interventions that will curb crime and improve well-being of communities, law
enforcement agencies are employing and deploying tools like CCTV for video surveillance in banks, residential
societies shopping malls,markets,roads almost everywhere to detect where and who was responsible for events
like robbery, rough driving, insolence, murder etc. Other surveillance system tools like phone tapping is used to
plot an event to catch a suspected person who is threatening an innocent or conspiring a violent activity over
a phone call which is being done voluntarily by authorities . Now analyzing both the scenarios in the case of
CCTV the crime(robbery,murder) has already occured and in the case of phone tapping there must be information
in advance about the suspected person who is going to commit a crime, Now to overcome this issue a system is
proposed to know in advance who is suspected and what conspiracy is being done over phone calls as well to
detect it automatically and report it to law enforcemet authorities.
Keywords - Deep learning ,Phone calls, Audio analysis ,Surveillance Systems,Threats
I. INTRODUCTION
Mass Surveillance practice began in world war 1 over 100 years ago with domestic spying of foreign
officials residing in different countries conducting surveillance, scandals and transferring of scurrilous
information have been key weapons of rival countries in WW1, WW2, cold wars and even now. Back then it
was the fight of countries, but now it is fight against criminals, terrorists, sleeper cells residing within
the country. Situations and scenarios like bank robbery, terrorist attacks, communal riots are
unpredictable and adversely involve a large group of innocent victims .Offences like kidnapping ,rapes, threats
can happen with anyone, Now here mass surveillance comes into picture with common community policing
strategies. This helps law agencies to tackle the issue by tracking suspects, integrating camera systems, deploying
officers, initiating neighbourhood crime watch and Mapping evolving crime patterns. Majorityof planning, threats
of such crimes occur over phone calls. To detect this a surveillance systems is proposed incorporating Deep
learning, to train the system with harsh words and threatening high pitch tone, enabling to detect mishap before
its occurrence. This will prevent potential crime from happening and save lives. Surveillance data has mostly
concentrated on video analysis, but here focus on audio analysis is done for the same. Audio analysis can take us
closer to semantics than video analysis could and also is computationally more efficient.
2. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
2
www.viva-technology.org/New/IJRI
II. RELATED WORKS
Philip Duncal et.al[1], describes about information retrieval, minimizing the development cycle,
identifying feature spaces, signal cleaning algorithms and pattern recognition methods, classification
and segmentation of soundtracks present similar challenges. These terminologies have substantial overlaps.
They are essentially pattern recognition tasks at different levels. Much of the literature deals with the
development of these algorithms in a combined manner, while fine-tuning algorithms to carry out different
tasks. Some of the methods used for recognition employ classification algorithms including support vector
machine (SVM) as a binary classifier , Hidden Markov Machine (HMM) which works on the bases of
prediction using previous data values, Artificial Neural Network (ANN) which is trained in a supervised
learning regime to map features/descriptors onto predetermined classes and decision trees. These are the
most commonly used types of classifiers, and may be employed individually and in combinations thereof.Support
Vector Machine (SVM) and Hidden Markov Model (HMM) were used to classify broadcast news audio into
6 classes: silence, pure speech, music, environmental sound, speech over music, and speech over environmental
sound classes
Eduard frank, et.al [2],the architecture is an adaptation of an image processing CNN, programmed in
Python using Keras model-level library and TensorFlow backend. The concept lays the basis of the classification
of emotions based on voice parameters is briefly presented. According to the obtained results, the model
achieves the mean accuracy of 71.33% enlisting six emotions (fear, sadness, happiness,anger,disgust,surprise),
which is comparable with performances reported in scientific literature. A person’s speech can be altered by
various changes in the autonomic nervous system and affective technologies can process this information
to recognize emotion. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast,
with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and
low-pitched speech. Some emotions have been found to be more easily computationally identified, such as
anger and happiness
Regunathan Radhakrishnan, et.al[3],this is a surveillance system for event detection cannot
completely rely on a supervised audio classification framework. In this paper, authors have proposed a
hybrid solution that consists of two parts; one that performs unsupervised audio analysis and another that performs
analysis using an audio classification framework obtained from off-line analysis and training. The proposed
system detects new kinds of suspicious audio events which are outliers against a background of usual activity. It
adapts via Gaussian Mixture Model(GMM) to model background sounds and updates the model incrementally
as latest audio data arrives. New types of suspicious events can be detected as deviants from this usual background
model. The results on data are promising surveillance systems based on an audio classification framework would
only be able to detect known kinds of suspicious activity like banging sounds and screaming. However, it
is also important to detect suspicious events that the system has not seen before. Towards that end, the author
proposes a hybrid solution that consists of two parts; one that performs unsupervised audio analysis and another
that performs analysis using an audio classification framework obtained from off-line analysis and training.
Arpit Shah, et.al[4], paper discusses a system in which an audio file of conversation between two
people from the EXOTEL (cloud telephony platform) dataset was taken as the basic input which was then
separated into different segments using VAD (voice activity detection). During VAD, It involves removing
the non-speech data like noise, pause-in between the conversation to separate the segments into different chunks
with voiced and unvoiced data. Since audio may involve different numbers of speakers, used agglomerative
clustering. It does segmenting of data into many small pieces. Each piece is assigned to a cluster. Audio is
segmented depending on the pauses that are present in the conversation, but if there is an overlap between two
people’s conversation then audio separation does not take place properly which in turn affects the clustering
process and furthermore miss-classifies the sentiments of the chunks as well. So instead of using a sliding video
and only relying on the pauses in the conversation, future scope would be to build an unsupervised model (Auto-
encoders) that performs VAD detection and further cleans out the segments with non-homogeneous data.
Cynthia Van Hee et.al [5], the Cyberspace is one of humanity's great inventions that bring great
benefits but also exposes us to cyber threats. Cyberbullying is common to many on social platforms. In this
paper authors have proposed a framework to detect cyberbullying messages in the form of text data using
deep neural networks and word embedding. They stack together the state-of-the-art Bert and Glove embedding to
improve the performance of the classifier. As a result, the itl outperforms the traditional machine learning methods
such as Logistic Regression and SVM . To perform specific word detection ,they did Initial Word Embedding.
3. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
3
www.viva-technology.org/New/IJRI
Word embedding is the process of representing each word as a real value vector. The embedding layer of there
models processes a fixed length sequence of words. In this study three methods are used for initializing word
embeddings: random, GloVe and SSWE. Using words embeddings during training improve the model to
learn task specific word embeddings. Task specific word embeddings can differentiate the style of
cyberbullying among different online platforms as well as topics.
Hao Hu, et.al [6], the GMM supervector based SVM is applied to this field with spectral features. A GMM
is trained for each emotional utterance, and the corresponding GMM supervector is used as the input feature for
SVM. Results on emotional speech database demonstrate that GMM supervector based SVM outperforms
standard GMM on speech emotion Recognition. Since the gender-dependent emotion recognition system is
preferred, they analysed the confusion between different emotions in condition of separate-gender subject System
proposes solution via Confusion matrix of GMM supervector based SVM for female subject and male
subject individually constituting 5 emotions anger; fear; happiness, neutral, sadness
Pavol Harár Radim ,Malay kishore Dutta et.at [7], in this paper the author has developed a method for
Speech Emotion Recognition (SER) using Deep Neural Network (DNN) architecture with convolutional, pooling
and fully connected layers. They used 3 class sets (angry, neutral, sad) of German Corpus containing 271
labelled recordings with a total length of 783 seconds. Audio files were split into 20ms segments without
overlap. Voice Activity Detection (VAD) algorithm used to eliminate blank segments and divided all data
into training (80%)validation (10%) and testing(10%) sets. DNN is optimized using Stochastic Gradient Descent.
As input authors used raw data without and feature selection. The trained model achieves test accuracy of
96.97%. The model works well, the model does not have any pre-given context, still it gives better results.
According to the author, providing context can improve the efficiency to the next level. This model can be
used to find out whether the person speaking is angry or happy. So that it can be sure that what is the intention of
any person detected speaking wrong suspected words.
Xavier Anguera Miro and Nicholas Evans et.al [8],Speaker diarization is the task of determining “who
spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown
number of speakers ,The analysis of speaker diarization performance as reported through the NIST Rich
Transcription evaluations. System incorporates HMM- UBM models and also Speech activity detection (SAD)
which involves the labeling of speech and nonspeech segments. SAD can have a significant impact on speaker
diarization performance , Author discusses importance of overlap detection incase where simultaneously multiple
speakers are talking.
III. METHODOLOGY
The proposed system is divided into three stages: The stage one is called speaker diarization. Speaker diarization
is the process of identifying caller1 and caller2 in a call and separating them into segments. Speaker diarization
works in three steps, first is voice activity detection(VAD), next is speaker change detection, and lastly speaker
clustering the results to get caller1 and caller2 segments. We have trained a deep neural network model to identify
the sentiment of the conversation. RAVDESS speech dataset is used for classification. Acoustic features such as
MFCC, STFT, Contrast, Mel Spectrum, Chroma and Tonnetz are extracted from the audio clips of the dataset.
We stack all the features to get a feature vector of 193 dimensions. The extracted features are passed to a DNN
composed of 3 hidden layers with the number of perceptrons to be 128, 128, 128 with RELU activation for all 3
hidden layers, 2 dropout layers of 0.2. Last layer being the softmax layer and adadelta optimizer. The model
trained in the Training phase is used to test the sentiment of the chunks generated after Speaker diarization. Every
chunk will yield sentiment which can help us understand the sentiment of the customer throughout the
conversation. Next stage is to identify threat keywords from the source audio. For this we use google speech API
to convert audio to text. Then we use the NLTK libraryto clean the text. Cleaninginvolves removingpunctuations,
stopwords, unnecessary blank space. This gives us a vector of cleaned words used in the conversation. We
compare this cleaned word vector with another vector of most commonly used threat keywords. Thus if a
keyword from cleaned words vector is present in the threat keywords vector, we identify it as a threat keyword in
the source audio. If the sentiments in the audio call are associated with threat and threat keywords are identified
in the conversation then it can be classified as a threat call. Fig 1. shows the system flow diagram.The source
audio file is first passed through a speaker diarization system. After speaker diarization each segment is passed
through DNN to find the sentiment of each segment. Next the source audio file is transcribed into a text file. This
text file is then sent to a TextSentimentAnalyser which checks if there are any threat keywords or not and if there
are which threat keywords are present in the conversation. In the source audio if there has been any sentiment
4. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
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National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
4
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associated with threat such as angry, fearful, etc. and at the same time if there were threat keywords in the same
source audio, the conversation will be classified as threat call and an alert will be sent to authorities.
Figure 1: system flow diagram.
Figure 2: sentiment classification
5. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
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IV. FIGURES AND TABLES
Table 1: Result analysis table
Model Training Test
CNN 90% 91%
XGBOOST 67.45% 45%
DNN 97% 93%
Table 1 shows the accuracy on training and test data of sentiment analysis using different models on 8
emotions(Clam, Happy, Sad, Angry, Fearful, Surprised, Neutral, Disgust)
Table 2: Precision, Recall and F1-scores for DNN model
Emotion Precision Recall F1-Score
Angry 0.93 0.97 0.95
Calm 0.94 0.95 0.94
Disgusting 0.97 0.95 0.96
Fearful 0.88 1.0 0.93
Happy 0.89 0.92 0.91
Sad 0.96 0.86 0.91
Surprise 0.92 0.87 0.89
Table 2 shows the Precision, Recall and F1-scores for each emotion using the DNN model.
Figure 3: GUI Surveillance system.
Fig. 3 shows the GUI of the updated main page of the application, the page lists detected threats along with
threat id, caller 1, caller 2, priority, date etc. this is connected to the database.
6. VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
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National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
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V. CONCLUSION
The proposed system will detect suspicious threat activity on phone calls, the system is capable of
extracting tones of a person like anger, shouting, abusing, harassment, blackmailing and also when people
use keywords like bomb, blast, murder, killing in english and hindi etc. A detailed report is generated indicating
threat or not threat and reports to police authorities enabling them to detect mishap or crime before its occurrence.
This project can be used by law enforcement agencies to trace criminals and prevent major incidents. This will
also eliminate the need to manually listen to calls for threats. Giving more importance to privacy, the system will
be very confidential, secure, encrypted and will not save any details if threats are not detected. The system will
prevent potential crime from happening and save lives.
Acknowledgements
We would like to express a deep sense of gratitude towards our guide Prof.Sunita Naik, Computer
Engineering Department for her constant encouragement and valuable suggestions. The work that we are able to
present is possible because of her timely guidance. We would like to pay gratitude to the panel of examiners Prof.
Sunita Naik, Prof. Dnyaneshwar Bhabad, & Prof. Vinit Raut for their time, effort they put to evaluate our work
and their valuable suggestions time to time. We would like to thank Project Head of the Computer Engineering
Department, Prof. Janhavi Sangoi for her support and coordination. We would like to thank the Head of the
Computer Engineering Department, Prof. Ashwini Save for her support and coordination. We are also grateful to
the teaching and the non-teaching staff of the Computer Engineering Department who lend their helping hands in
providing continuous support.
REFERENCES
[1] Philip Duncal, Duraid Mohammed “ Audio information extraction from arbitrary sound recordings” Audio Engineering Society
(AES) ,2014 , 22nd International Congress on Sound and Vibration (ICSV22) and AES 136th Convention
[2] Eduard frantic ,Monica Dascalu , “Voice Based Emotion Recognition with Convolutional Neural Networks for Companion Robots”
,(ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY )Volume 20, Number 3, 2017, 222–240.
[3] Regunathan Radhakrishnan, Ajay Divakaran, “Audio Analysis for surveillance applications for elevators” Mitsubishi Electric Research
Labs 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.
[4] Arpit Shah,Shivani , Firodiya ”Audio Sentiment Analysis after a Single-Channel Multiple Source Separation” Indiana University
Bloomington,2019.
[5] Cynthia Van Hee , Gilles Jacobs “Automatic Detection of Cyberbullying in Social Media Text” AMiCA (Automatic Monitoring of
Cyberspace Applications), 2018, rXiv:1801.05617v1 [cs.CL]
[6] Hao Hu, Ming-XingXu, and Wei Wu, “GMM Supervector Based SVM With Spectral Features For Speech Emotion
Recognition” The international Conference on Acoustics, Speech, & Signal Processing (ICASSP),IEEE ,2007,1-4244-0728-1/07
[7] Radim Burger and Malay Kishore Dutta ”Speech Emotion Recognition with Deep Learning” 4th International Conference on
Signal Processing and Integrated Networks (SPIN) , IEEE ,2017, 978-1-5090-2797-2/17/
[8] Xavier Anguera Miro and Nicholas Evans “Speaker Diarization: A Review of Recent Research” IEEE Transactions On Audio, Speech,
And Language Processing, IEEE Vol. 20, No. 2 ,February 2012 ,1558-7916/
[9] James Albert Cornel, Carl Christian Pablo,“Cyberbullying Detection for Online Games Chat Logs using Deep Learning,” IEEE , 2019,
978-1-7281-3044
[10] Alexandru Lucian Georgescu , Horia Cucu “Automatic Annotation of Speech Corpora using Complementary GMM and DNN Acoustic
Models,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, IEEE, 2018 , 978-1-5386-4695
[11] Fayek, H. M., M. Lech, and L. Cavedon. "Towards real-time speech emotion recognition using deep neural networks." Signal Processing
and Communication Systems (ICSPCS), IEEE, 2015, 978-1-4673-8118
[12] Eesung Kimand ,Jong Won Shin, ”DNN-based emotion recognition based on bottleneck acoustic features and lexical features”, ICASSP
,IEEE, 2019, 978-1-5386-4658/
[13] Starlet Ben Alex and Ben P. Bab,”Utterance Syllable Level Prosodic Features for Automatic Emotion Recognition”, Recent Advances
in Intelligent Computational Systems (RAICS), IEEE,2018
![VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 4 (2021)
ISSN(Online): 2581-7280 Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
2
www.viva-technology.org/New/IJRI
II. RELATED WORKS
Philip Duncal et.al[1], describes about information retrieval, minimizing the development cycle,
identifying feature spaces, signal cleaning algorithms and pattern recognition methods, classification
and segmentation of soundtracks present similar challenges. These terminologies have substantial overlaps.
They are essentially pattern recognition tasks at different levels. Much of the literature deals with the
development of these algorithms in a combined manner, while fine-tuning algorithms to carry out different
tasks. Some of the methods used for recognition employ classification algorithms including support vector
machine (SVM) as a binary classifier , Hidden Markov Machine (HMM) which works on the bases of
prediction using previous data values, Artificial Neural Network (ANN) which is trained in a supervised
learning regime to map features/descriptors onto predetermined classes and decision trees. These are the
most commonly used types of classifiers, and may be employed individually and in combinations thereof.Support
Vector Machine (SVM) and Hidden Markov Model (HMM) were used to classify broadcast news audio into
6 classes: silence, pure speech, music, environmental sound, speech over music, and speech over environmental
sound classes
Eduard frank, et.al [2],the architecture is an adaptation of an image processing CNN, programmed in
Python using Keras model-level library and TensorFlow backend. The concept lays the basis of the classification
of emotions based on voice parameters is briefly presented. According to the obtained results, the model
achieves the mean accuracy of 71.33% enlisting six emotions (fear, sadness, happiness,anger,disgust,surprise),
which is comparable with performances reported in scientific literature. A person’s speech can be altered by
various changes in the autonomic nervous system and affective technologies can process this information
to recognize emotion. As an example, speech produced in a state of fear, anger, or joy becomes loud and fast,
with a higher and wider range in pitch, whereas emotions such as sadness or tiredness generate slow and
low-pitched speech. Some emotions have been found to be more easily computationally identified, such as
anger and happiness
Regunathan Radhakrishnan, et.al[3],this is a surveillance system for event detection cannot
completely rely on a supervised audio classification framework. In this paper, authors have proposed a
hybrid solution that consists of two parts; one that performs unsupervised audio analysis and another that performs
analysis using an audio classification framework obtained from off-line analysis and training. The proposed
system detects new kinds of suspicious audio events which are outliers against a background of usual activity. It
adapts via Gaussian Mixture Model(GMM) to model background sounds and updates the model incrementally
as latest audio data arrives. New types of suspicious events can be detected as deviants from this usual background
model. The results on data are promising surveillance systems based on an audio classification framework would
only be able to detect known kinds of suspicious activity like banging sounds and screaming. However, it
is also important to detect suspicious events that the system has not seen before. Towards that end, the author
proposes a hybrid solution that consists of two parts; one that performs unsupervised audio analysis and another
that performs analysis using an audio classification framework obtained from off-line analysis and training.
Arpit Shah, et.al[4], paper discusses a system in which an audio file of conversation between two
people from the EXOTEL (cloud telephony platform) dataset was taken as the basic input which was then
separated into different segments using VAD (voice activity detection). During VAD, It involves removing
the non-speech data like noise, pause-in between the conversation to separate the segments into different chunks
with voiced and unvoiced data. Since audio may involve different numbers of speakers, used agglomerative
clustering. It does segmenting of data into many small pieces. Each piece is assigned to a cluster. Audio is
segmented depending on the pauses that are present in the conversation, but if there is an overlap between two
people’s conversation then audio separation does not take place properly which in turn affects the clustering
process and furthermore miss-classifies the sentiments of the chunks as well. So instead of using a sliding video
and only relying on the pauses in the conversation, future scope would be to build an unsupervised model (Auto-
encoders) that performs VAD detection and further cleans out the segments with non-homogeneous data.
Cynthia Van Hee et.al [5], the Cyberspace is one of humanity's great inventions that bring great
benefits but also exposes us to cyber threats. Cyberbullying is common to many on social platforms. In this
paper authors have proposed a framework to detect cyberbullying messages in the form of text data using
deep neural networks and word embedding. They stack together the state-of-the-art Bert and Glove embedding to
improve the performance of the classifier. As a result, the itl outperforms the traditional machine learning methods
such as Logistic Regression and SVM . To perform specific word detection ,they did Initial Word Embedding.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)