Speech recognition is always being an all-time trendy topic for discussion and also for researches and we see a major application in our life. This paper provides the work done on the application of Hidden Markov model to implement isolated word speech recognition on MATLAB and to develop and train the system for set of self-selective words for specific user (user dependent) to get maximum efficiency in word recognition system. Which uses the forward and Baum-welch algorithm and fitting Gaussian of the Baum-welch algorithm for all the iteration perform. We use a sample of 7 alphabets which are recorded in 15 different ways giving total of 105 word to use for training with each word with 15 variations. This system can be used in real world in system security using voice security system and mainly for children and impaired people.
An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelli...ijtsrd
This study proposes Artificial Intelligence AI based path loss prediction models for the suburban areas of Abuja, Nigeria. The AI based models were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
An Artificial Intelligence Approach to Ultra High Frequency Path Loss Modelli...ijtsrd
This study proposes Artificial Intelligence AI based path loss prediction models for the suburban areas of Abuja, Nigeria. The AI based models were created on the bases of two deep learning networks, namely the Adaptive Neuro Fuzzy Inference System ANFIS and the Generalized Radial Basis Function Neural network RBF NN . These prediction models were created, trained, validated and tested for path loss prediction using path loss data recorded at 1800MHz from multiple Base Transceiver Stations BTSs distributed across the areas under investigation. Results indicate that the ANFIS and RBF NN based models with Root Mean Squared Error RMSE values of 5.30dB and 5.31dB respectively, offer greater prediction accuracy over the widely used empirical COST 231 Hata, which has an RMSE of 8.18dB. Deme C. Abraham ""An Artificial Intelligence Approach to Ultra-High Frequency Path Loss Modelling of the Suburban Areas of Abuja, Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30227.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30227/an-artificial-intelligence-approach-to-ultra-high-frequency-path-loss-modelling-of-the-suburban-areas-of-abuja-nigeria/deme-c-abraham
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Semantic Mask for Transformer Based End-to-End Speech RecognitionWhenty Ariyanti
The attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks. Inspired by SpecAugment and BERT, this study proposed a semantic mask based regularization for training such kind of end-to-end (E2E) model. While this approach is applicable to the encoder-decoder framework with any type of Neural Network architecture, then study the transformer-based model for ASR and perform experiments on LibriSpeech 960h and TedLium2 dataset and achieve state-of-the-art performance on the test set in the scope of E2E models.
This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.
In the software industry, two software engineering development best practices coexist: open-source and
closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a
proprietary code that only the owner can access. Software reliability is crucial in the industry when a new
product or update is released. Applying meta-heuristic optimization algorithms for closed-source software
reliability prediction has produced significant and accurate results. Now, open-source software dominates
the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as
a quality indicator - would greatly help solve the open-source software reliability growth- modelling
problem.
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
NYAI #5 - Fun With Neural Nets by Jason YosinskiRizwan Habib
Fun With Neural Nets - (Jason Yosinski, Researcher at Geometric Intelligence)
Jason Yosinski is a researcher at Geometric Intelligence, where he uses neural networks and machine learning to build better AI. He was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. When not doing research, Mr. Yosinski enjoys tricking middle school students into learning math while they play with robots.
Jason will talk about how deep neural networks have recently been making a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but hard for machines, like playing Atari games or identifying lions or jaguars in photos. But how do these neural nets actually work? What do they learn? This turns out to be a surprisingly tricky question to answer — surprising because we built the darn things, but tricky because the networks are so large and have many millions of connections that effect complex computation which is hard to interpret. Trickiness notwithstanding, in this talk we’ll see what we can learn about neural nets by looking at a few examples of networks in action and experiments designed to elucidate network behavior.
Website: http://yosinski.com/
A comparison of different support vector machine kernels for artificial speec...TELKOMNIKA JOURNAL
As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
Using genetic algorithms and simulation as decision support in marketing stra...infopapers
F.Stoica, L.F.Cacovean, Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, Proceedings of the 9th WSEAS International Conference on SIMULATION, MODELLING AND OPTIMIZATION (SMO ‘09), Budapest Tech, Hungary, September 3-5, ISSN: 1790-2769 ISBN:978-960-474-113-7, pp. 435-439, 2009
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
DEVELOPMENT OF TODDLER FAMILY CADRE TRAINING BASED ON ANDROID APPLICATIONS IN...AM Publications
Toddler family cadre is a community members work voluntarily in fostering and providing information to parents of toddlers about how to properly care for children. Toddler Family cadre desperately need training to increase their skills. There are still a few Toddler family cadres who get training so that the knowledge and skills of parents and other family members in developing toddlers' growth through physical stimulation, motoric intelligence, emotional and social economy as well as possible are still lacking. The purpose of this study is to develop an Android- assisted Toddler family cadre training model in Demak. This research is research in tian research and development. The research location was in Demak Regency. Toddler family cadres became the object of this research. Development of Toddler family cadre training models assisted by Android in Demak is feasible to be used as an effort to improve Toddler Family cadres' capabilities.
TESTING OF COMPOSITE ON DROP-WEIGHT IMPACT TESTING AND DAMAGE IDENTIFICATION ...AM Publications
In recent years the use of composite materials in structural components has become increasingly common in a wide range of engineering applications. Composite materials offer numerous advantages over more conventional materials because of their superior specific properties, but a serious obstacle to a more widespread use of these materials is their high sensitivity to localized impact loading. This paper presents an experimental study to assess the impact response of drop weight impact tests on fiber reinforced polymer composites with deferent load and damage identification of composite using Non-destructive testing techniques ultrasonic testing (UT) C scan. In the study includes checking the strength of the specimen, plotting of graphs between the height and the impact energy obtained and tabulating the results after conducting the various functional tests.
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This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.
In the software industry, two software engineering development best practices coexist: open-source and
closed-source software. The former has a shared code that anyone can contribute, whereas the latter has a
proprietary code that only the owner can access. Software reliability is crucial in the industry when a new
product or update is released. Applying meta-heuristic optimization algorithms for closed-source software
reliability prediction has produced significant and accurate results. Now, open-source software dominates
the landscape of cloud-based systems. Therefore, providing results on open- source software reliability - as
a quality indicator - would greatly help solve the open-source software reliability growth- modelling
problem.
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
NYAI #5 - Fun With Neural Nets by Jason YosinskiRizwan Habib
Fun With Neural Nets - (Jason Yosinski, Researcher at Geometric Intelligence)
Jason Yosinski is a researcher at Geometric Intelligence, where he uses neural networks and machine learning to build better AI. He was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. When not doing research, Mr. Yosinski enjoys tricking middle school students into learning math while they play with robots.
Jason will talk about how deep neural networks have recently been making a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but hard for machines, like playing Atari games or identifying lions or jaguars in photos. But how do these neural nets actually work? What do they learn? This turns out to be a surprisingly tricky question to answer — surprising because we built the darn things, but tricky because the networks are so large and have many millions of connections that effect complex computation which is hard to interpret. Trickiness notwithstanding, in this talk we’ll see what we can learn about neural nets by looking at a few examples of networks in action and experiments designed to elucidate network behavior.
Website: http://yosinski.com/
A comparison of different support vector machine kernels for artificial speec...TELKOMNIKA JOURNAL
As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
Holistic Approach for Arabic Word RecognitionEditor IJCATR
Optical Character Recognition (OCR) is one of the important branches. One segmenting words into character is one of the
most challenging steps on OCR. As the results of advances in machine speeds and memory sizes as well as the availability of large
training dataset, researchers currently study Holistic Approach “recognition of a word without segmentation”. This paper describes a
method to recognize off-line handwritten Arabic names. The classification approach is based on Hidden Markov models.. For each
Arabic word many HMM models with different number of states have been trained. The experiments result are encouraging, it also
show that best number of state for each word need careful selection and considerations.
Using genetic algorithms and simulation as decision support in marketing stra...infopapers
F.Stoica, L.F.Cacovean, Using genetic algorithms and simulation as decision support in marketing strategies and long-term production planning, Proceedings of the 9th WSEAS International Conference on SIMULATION, MODELLING AND OPTIMIZATION (SMO ‘09), Budapest Tech, Hungary, September 3-5, ISSN: 1790-2769 ISBN:978-960-474-113-7, pp. 435-439, 2009
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
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Toddler family cadre is a community members work voluntarily in fostering and providing information to parents of toddlers about how to properly care for children. Toddler Family cadre desperately need training to increase their skills. There are still a few Toddler family cadres who get training so that the knowledge and skills of parents and other family members in developing toddlers' growth through physical stimulation, motoric intelligence, emotional and social economy as well as possible are still lacking. The purpose of this study is to develop an Android- assisted Toddler family cadre training model in Demak. This research is research in tian research and development. The research location was in Demak Regency. Toddler family cadres became the object of this research. Development of Toddler family cadre training models assisted by Android in Demak is feasible to be used as an effort to improve Toddler Family cadres' capabilities.
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The aim of this paper is to help the blind people to identify and catch the public transport vehicles with the help of Light Fidelity technology. It is a Navigation aid. When the bus arrives at the bus stand, transmitter in the bus transmits the light signals and receiver in the stick, receives the light signals and a sound signal is generated through the speaker present in the stick. The sound message contains the bus number and the destination of the bus. In addition to this, if the person is absconded or lost, details of the location will be sent to his/her family members by pressing a button. This is made possible with the help of Global System for Mobile (GSM). Finally, presence of water can be detected along the blind person’s path, with the help of water sensors.
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A digital radiography delivers a radiation dose to patients; therefore it poses potential risk to the patients. One effort to reduce dose is carried out using a radiation filter, e.g. Silicone Rubber (SR) sheet. The purpose of this research was to determine the impact of the SR sheet on the high contrast objects (HCO) and the low contrast objects (LCO). The dose reduction was determined from attenuation x-rays before and after using the SR sheet. Assessment of HCO and LCO was observed from CDR TOR phantom at tube voltage of 48 kVp and tube current of 8 mAs. The physical parameter to assess image quality was the Signal to Noise Ratio (SNR) value in LCO. The maximum x-ray attenuation using the SR sheet is 48.82%. The visibility of the HCO remains the same, namely 16 objects; however the LCO slighly decreases from 14 objects to 13 objects after using the SR sheet. The SNR value decreases with an average value of 15.17%.Therefore, the SR sheet as a alternative filter has no effect on the HCO and has realtively little effect on the LCO. Thus, the SR sheet potentially is used for radiation protection in patients, especially on examinations that do not require low contrast resolution.
UTILIZATION OF IMMUNIZATION SERVICES AMONG CHILDREN UNDER FIVE YEARS OF AGE I...AM Publications
Immunization is the key strategy to curb communicable diseases which are the number one killer of children under five. Immunization prevents mortalities of approximating three million children under five annually. This study aimed to assess utilization of immunization services among children under five of age in Kirinyaga County, Kenya.
REPRESENTATION OF THE BLOCK DATA ENCRYPTION ALGORITHM IN AN ANALYTICAL FORM F...AM Publications
The article presents the study of cryptographic transformations of the Kuznyechik algorithm in relation to differential analysis and the translation of their representations into a more convenient form for cryptanalysis. A simplification of the type of transformations of the algorithm to algebraic the form, in which cryptanalysis software will be more effective. Since the description of the algorithm in the analytical form allows for 16 cycles of execution of the shift register with linear feedback, each of which will be carried out 16 operations of multiplication and 15 operations of addition, reduced to 16 multiplying and 15 the operations of addition. The result is an algebraic form of a linear transformation (from a shift register with linear feedback to the multiplication of the matrix in a finite field). In the future, the algebraic type of transformation can be used to effectively carry out differential cryptanalysis.
Optical character recognition (OCR) is process of classification of optical patterns contained in a digital image. The process of OCR Recognition involves several steps including pre-processing, segmentation, feature extraction, classification. Pre-processing is for done the basic operation on input image like noise reduction which remove the noisy signal from image. Segmentation stage for segment the given image into line by line and segment each character from segmented line. Future extraction calculates the characteristics of character. A Radial Basis Function Neural Network (RBFNN) is used to classification contains the database and does the comparison.
Surveillance refers to the task of observing a scene, often for lengthy periods in search of particular objects or particular behaviour. This task has many applications, foremost among them is security (monitoring for undesirable behaviour such as theft or vandalism), but increasing numbers of others in areas such as agriculture also exist. Historically, closed circuit TV (CCTV) surveillance has been mundane and labour Intensive, involving personnel scanning multiple screens, but the advent of reasonably priced fast hardware means that automatic surveillance is becoming a realistic task to attempt in real time. Several attempts at this are underway.
SIMULATION OF ATMOSPHERIC POLLUTANTS DISPERSION IN AN URBAN ENVIRONMENTAM Publications
Interest in air pollution investigation of urban environment due to existence of industrial and commercial activities along with vehicular emission and existence of buildings and streets which setup natural barrier for pollutant dispersion in the urban environment has increased. The air pollution modelling is a multidisciplinary subject when the entire cities are taken under consideration where urban planning and geometries are complex which needs a large software packages to be developed like Operational Street Pollution Model (OSPM), California Line Source model (CALINE series) etc. On overviewing various works it can be summarized that the air pollutant dispersion in urban street canyons and all linked phenomenon such as wind flow, pollutant concentrations, temperature distribution etc. generally depend on wind speed and direction, building heights and density, road width, source and intensity of air pollution, meteorological variables like temperature, humidity etc. A unique and surprising case is observed every time on numerous combinations of these factors. The main aim of this study is to simulate the atmospheric pollutant dispersion for given pollutant like carbon monoxide, sulphur dioxide and nitrogen dioxide and given atmospheric conditions like wind speed and direction. Computational Fluid Dynamics (CFD) simulation for analysing the atmospheric pollutant dispersion is done after natural airflow analysis. Volume rendering is done for variables such as phase 2 volume fraction and velocity with resolution as 250 pixels per inch and transparency as 20%. It can be observed that all the three pollutant namely nitrogen dioxide, sulphur dioxide and carbon monoxide the phase 2 volume fraction changes from 0 to 1. The wind velocity changes from 3.395×10-13 m/s to 1.692×102 m/s. The dispersion of pollutants follow the sequence Sulphur dioxide>Carbon monoxide>Nitrogen dioxide.
PREPARATION AND EVALUATION OF WOOL KERATIN BASED CHITOSAN NANOFIBERS FOR AIR ...AM Publications
In this article, we have extracted keratin from deccani wool waste and prepared the wool keratin based Chitosan nanofibers by electrospinning technique. The prepared nanofibers mat were prepared with different weight percent ratio like 1wt.%, 3wt.% and 5wt.% with respect to polymer i.e Chitosan. The physicochemical and filtration properties of wool keratin based Chitosan nanofibers were studied. Wool keratin based Chitosan nanofibers were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC) and scanning electron microscopy (FESEM). The filtration efficiency of keratin Chitosan nanofibers were investigated through DOP test and heavy metal removal capacity of evaluated through Atomic absorption spectroscopy. FTIR results were showed that Keratin gets compatible with Chitosan. XRD patterns revealed keratin was in crystalline nature and increase the crystalline nature of Chitosan nanofibers. FESEM images showed that uniform nanofibers generation with average fiber diameter 80nm. Nanofibers filtration efficiency against a particulate matter in air was obtained more than 99.53% and excellent property of removal of heavy metal.
ANALYSIS ON LOAD BALANCING ALGORITHMS IMPLEMENTATION ON CLOUD COMPUTING ENVIR...AM Publications
Cloud computing means storing and accessing data and programs over the Internet instead of your computer's hard drive. The cloud is just a metaphor for the Internet. The elements involved in cloud computing are clients, data center and distributed server. One of the main problems in cloud computing is load balancing. Balancing the load means to distribute the workload among several nodes evenly so that no single node will be overloaded. Load can be of any type that is it can be CPU load, memory capacity or network load. In this paper we presented an architecture of load balancing and algorithm which will further improve the load balancing problem by minimizing the response time. In this paper, we have proposed the enhanced version of existing regulated load balancing approach for cloud computing by comping the Randomization and greedy load balancing algorithm. To check the performance of proposed approach, we have used the cloud analyst simulator (Cloud Analyst). Through simulation analysis, it has been found that proposed improved version of regulated load balancing approach has shown better performance in terms of cost, response time and data processing time.
A MODEL BASED APPROACH FOR IMPLEMENTING WLAN SECURITY AM Publications
This paper presents various security features and configurations commonly implemented in WLANs and their aggregated security levels and then proposes a model that enables implementation and evaluation of WLAN security
DATA MINING WITH CLUSTERING ON BIG DATA FOR SHOPPING MALL’S DATASETAM Publications
Big Data is the extremely large sets of data that their sizes are beyond the ability of capturing, managing, processing and storage by most software tools and people which is ever increasing day-by-day. In most enterprise scenarios the data is too big or it moves too fast that extremely exceeds current processing capacity. The term big data is also used by vendors, may refer to the technology which includes tools and processes that an organization requires to handle the large amounts of data and storage facilities. This advancement in technology leads to make relationship marketing a reality for today’s competitive world. But at the same time this huge amount of data cannot be analyzed in a traditional manner, by using manual data analysis. For this, technologies such as data warehousing and data mining have made customer relationship management as a new area where business firms can gain a competitive advantage for identifying their customer behaviors and needs. This paper mainly focuses on data mining technique that performs the extraction of hidden predictive information from large databases and organizations can identify valuable customers and predicts future user behaviors. This enables different organizations to make proactive, knowledge-driven decisions. Data mining tools answer business questions that in the past were too time-consuming, this makes customer relationship management possible. For this in this paper, we are trying explain the use of data mining technique to accomplish the goals of today’s customer relationship management and Decision making for different companies that deals with big data.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
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