This document compares several machine learning classification algorithms. It first provides background on machine learning and describes common algorithms like linear regression, support vector machines, and decision trees. It then outlines an experimental framework in Python using libraries like Pandas, Scikit-Learn, and Matplotlib. Various classification algorithms are applied to a dataset and their test and train errors are calculated and compared to determine the most accurate algorithm. The proposed algorithm is found to have the lowest test and train errors compared to other algorithms like ridge regression, KNN, Bayesian regression, decision trees, and SVM.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
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.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
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.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
A Comprehensive review of Conversational Agent and its prediction algorithmvivatechijri
There is an exponential increase in the use of conversational bots. Conversational bots can be
described as a platform that can chat with people using artificial intelligence. The recent advancement has
made A.I capable of learning from data and produce an output. This learning of data can be performed by using
various machine learning algorithm. Machine learning techniques involves construction of algorithms that can
learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning
algorithm that are used in conversational bot.
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
Analysis of Agile and Multi-Agent Based Process Scheduling Modelirjes
As an answer of long growing frustration of waterfall Software development life cycle concepts,
agile software development concept was evolved in 90’s. The most popular agile methodologies is the Extreme
Programming (XP). Most software companies nowadays aim to produce efficient, flexible and valuable
Software in short time period with minimal costs, and within unstable, changing environments. This complex
problem can be modeled as a multi-agent based system, where agents negotiate resources. Agents can be used to
represent projects and resources. Crucial for the multi-agent based system in project scheduling model, is the
availability of an effective algorithm for prioritizing and scheduling of task. To evaluate the models, simulations
were carried out with real life and several generated data sets. The developed model (Multi-agent based System)
provides an optimized and flexible agile process scheduling and reduces overheads in the software process as it
responds quickly to changing requirements without excessive work in project scheduling.
AnAccurate and Dynamic Predictive Mathematical Model for Classification and P...inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Mathematical models and algorithms challengesijctcm
This paper succinctly illustrates challenges encountered when modelling systems mathematically.
Mathematical modelling entirely entails math symbols, numbers and relations forming a functional
equation. These mathematical equations can represent any system of interests, also provides ease computer
simulations. Mathematical models are extensively utilized in different fields i.e. engineering, by scientists,
and analysts to give a clear understanding of the problem. Modelling contributed a lot since inversion of
the concept. Simple and complex structures erected as a result of modelling. In that sense modelling is an
important part of engineering. It can be referred to as the primary building block of every system. A
complex model however is not an ideal solution. Engineers have to be cautious not to discard all
information as this might render the designed model useless – as detailed in this paper the model should be
simple with all necessary and relevant data. Basically the purpose of this paper is to show the importance
and clearly explain in detail challenges encountered when modelling
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
A Comprehensive review of Conversational Agent and its prediction algorithmvivatechijri
There is an exponential increase in the use of conversational bots. Conversational bots can be
described as a platform that can chat with people using artificial intelligence. The recent advancement has
made A.I capable of learning from data and produce an output. This learning of data can be performed by using
various machine learning algorithm. Machine learning techniques involves construction of algorithms that can
learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning
algorithm that are used in conversational bot.
Using Data Mining to Identify COSMIC Function Point Measurement Competence IJECEIAES
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
Analysis of Agile and Multi-Agent Based Process Scheduling Modelirjes
As an answer of long growing frustration of waterfall Software development life cycle concepts,
agile software development concept was evolved in 90’s. The most popular agile methodologies is the Extreme
Programming (XP). Most software companies nowadays aim to produce efficient, flexible and valuable
Software in short time period with minimal costs, and within unstable, changing environments. This complex
problem can be modeled as a multi-agent based system, where agents negotiate resources. Agents can be used to
represent projects and resources. Crucial for the multi-agent based system in project scheduling model, is the
availability of an effective algorithm for prioritizing and scheduling of task. To evaluate the models, simulations
were carried out with real life and several generated data sets. The developed model (Multi-agent based System)
provides an optimized and flexible agile process scheduling and reduces overheads in the software process as it
responds quickly to changing requirements without excessive work in project scheduling.
AnAccurate and Dynamic Predictive Mathematical Model for Classification and P...inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Mathematical models and algorithms challengesijctcm
This paper succinctly illustrates challenges encountered when modelling systems mathematically.
Mathematical modelling entirely entails math symbols, numbers and relations forming a functional
equation. These mathematical equations can represent any system of interests, also provides ease computer
simulations. Mathematical models are extensively utilized in different fields i.e. engineering, by scientists,
and analysts to give a clear understanding of the problem. Modelling contributed a lot since inversion of
the concept. Simple and complex structures erected as a result of modelling. In that sense modelling is an
important part of engineering. It can be referred to as the primary building block of every system. A
complex model however is not an ideal solution. Engineers have to be cautious not to discard all
information as this might render the designed model useless – as detailed in this paper the model should be
simple with all necessary and relevant data. Basically the purpose of this paper is to show the importance
and clearly explain in detail challenges encountered when modelling
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep
learning technique is to improve the implementation rate based upon the capability of the society. The
main objective of the proposed system is to apply the deep learning using data driven approach for
controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.
Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not
get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the
chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs.Through this system, huge volume of data’s that are generated by the system will also get control.
ANALYSIS OF SYSTEM ON CHIP DESIGN USING ARTIFICIAL INTELLIGENCEijesajournal
Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
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.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.
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.
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.