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Predicting Code Smells: A Comparative Study of machine learning Classifiers for God Class and Data Class

Predicting Code Smells: A Comparative Study of Machine Learn...

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international conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Kiran Kumar Sahu Rasmita Panigrahi Vishal Kumar Swain Neelamadhab Padhy Department of CSE GIET UNIVERSITY Gunupur Odisha India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Code Smells often indicate underlying problems that can degrade various aspects of software quality. It refers to the area in the source code which needs improvement. software with Code Smell may lead to high maintenance costs. Code Smells are not errors or bugs; rather addressing them can enhance the code quality. Code with Smells may lead to software defects in the future. Objective- This paper aims to predict specific Code Smells, namely God Class and Data Class, present in the source code and compare the performances of various machine learning models. Methodology- We compared the performance of four traditional machine learning classifiers (Decision Tree, Naive Bayes, Logistic Regression, and K-Nearest Neighbor) with five ensemble classifiers (Random Forest, Bagging with Decision Tree, Gradient Boosting, AdaBoost, and XGBoost). Result & Conclusion- Our analysis indicates that the existence of God Class is more prevalent than Data Class in the examined code, with accuracy as the primary performance metric. Our experimental observation reveals that XGBoost outperforms predicting the Code Smells in terms of all performance measures i.e. 100%.

关键词： Codes Power measurement Source coding software quality Signal processing Predictive models Decision trees software measurement Random forests software engineering

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ML-Orchestrated Malware Detection Framework for Windows APIs in Critical Infrastructure Systems

ML-Orchestrated Malware Detection Framework for Windows APIs...

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Computer, Communication and Control (IC4), international conference on

作者： Shayalkumar Vaghasiya Rajesh Gupta Santoshinee Mohapatra Sudeep Tanwar Deepak Garg Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat India School of Computer Science and AI SR University Warangal Telangana

The security of critical infrastructure systems is crucial as they provide basic amenities and services necessary for functioning of a community, also they represent important part of country’s economy. An attack on this critical infrastructure systems can have significant consequences. In this paper we discuss the This paper addresses the increasing security challenges in critical infrastructure systems due to cyber threats, particularly malware and propose a framework based on machine learning for robust malware classification. Our proposed framework includes a dataset of Windows API functions used by the software, preprocessing techniques, and the XGBoost model which achieves an accuracy of 86.98%. Using this model, we can detect and categorize five different types of malwares to provide security against malware attacks. The results show model’s ability to successfully classify different kinds of malware. Realtime monitoring is another feature of this framework which is important for actively protecting critical infrastructure systems.

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Optimizing IT Governance and Project Management in software Development through AI Integration and COBIT 2019 Framework

Optimizing IT Governance and Project Management in Software ...

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Technology Innovation and Its Applications (ICTIIA), international conference on

作者： Andy Teguh Joko Slamet Hartono Saputro Kelly Sungkono Riyanarto Sarno Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

In the modern business environment, efficient time management and robust IT governance are crucial due to rapid technological advancements. Effective governance and resource management are imperative for organizations aiming to optimize performance. This research uses AI techniques and the COBIT 2019 framework to optimize IT governance in software development projects. Quantitative methods are employed to analyze factors causing scheduling delays in projects using machine learning algorithms, focusing on identifying factors that impact project timelines. Data from a previous study on software cost estimation involves 120 projects from 42 organizations. Data preprocessing techniques are applied to handle missing data and scale features. Feature engineering is conducted to identify critical factors affecting project completion times. We evaluate various machine learning models for robust analysis of project delay factors, and we find that CatBoost provides reliable project duration predictions. Model performance was evaluated using MAE, RMSE, R2, and MAPE. Integrating AI with COBIT 2019 enhances project duration estimation accuracy and strengthens IT governance. This combined approach improves project efficiency and aligns IT initiatives with organizational goals.

关键词： Technological innovation Estimation Project management Organizations Scheduling software Delays software reliability Resource management software development management

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Ethical Considerations of Bias and Fairness in AI Models

Ethical Considerations of Bias and Fairness in AI Models

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Disruptive Technologies (ICDT), international conference on

作者： Sarvachan Verma Neha Paliwal Kanchan Yadav P. C. Vashist Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad UP India Department of CSE(Data Science) Raj Kumar Goel Institute of Technology Ghaziabad UP India Department of Mechanical Engineering GLA University Mathura UP India Department of Information Technology GL Bajaj Institute of Technology and Management Greater Noida Up India

The ethical implications of artificial intelligence (AI) are becoming increasingly important. AI systems have the potential to incite bias against certain races and genders, either through programming choices or through the data collected to train the device. Thus, it is imperative to address ethical issues pertaining to justice and bias in order to guarantee the most responsible and optimal application of AI in society. Integrating AI ethics into software development processes is one way to deal with bias and equality in machine learning models. AI developers also have to deal with bias through length of information, in addition to programming concerns. Determining the lifespan of records entails making deliberate decisions about record entry that reduce prejudice and ensure fairness. It entails getting rid of protected characteristics that could have biased effects, such race or gender. Diversifying the kind of records that are included in models can also help to lessen the risk of bias.

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A Federated learning Algorithm Based on Combination of Prototype and Comparative learning

A Federated Learning Algorithm Based on Combination of Proto...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Yanwei Chen Wenbo Huang Software Institute Nanjing University Nanjing China School of Communication Engineering Jilin University Changchun China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

Federated learning is a modern distributed machine learning paradigm that has garnered a lot of interest because of its benefits for collaborative model training and data privacy. However, federated learning is confronted with many challenges, including client data distribution heterogeneity and model drift. These factors place greater demands on model optimization and generalization capabilities. Traditional federated learning methods typically rely on gradient exchange and aggregation of global model parameters. This paper puts forward a privatized federated learning algorithm that combines prototype federated learning and model comparison federated learning, aiming to use prototype exchange and comparison learning techniques to increase the federated learning models' training effectiveness and classification accuracy. This algorithm significantly reduces the communication overhead by exchanging abstract class prototypes between the client and the server. Additionally, it uses comparison learning to enhance the loss algorithm, which improves the model's capacity for generalization. This, in turn, reduces the representation drift between the local and global models, thus improving the learning effectiveness of the model.

关键词： Training Accuracy Federated learning Scalability Prototypes Contrastive learning Data models Classification algorithms Sensors Servers

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A Systematic Literature Review on Digital Forensic Investigation on Android Devices 2

A Systematic Literature Review on Digital Forensic Investiga...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Almuqren, Almaha Alsuwaelim, Hanan Hafizur Rahman, M.M. Ibrahim, Adamu A. Department of Computer Networks and Communications King Faisal University Saudi Arabia Saudi Arabia Department of Computer Science KICT International Islamic University Malaysia IIUM Kuala Lumpur Malaysia

Mobile forensics, particularly in the Android ecosystem, is a rapidly evolving field that demands continuous advancements to address the growing complexity and diversity of mobile devices. This article emphasizes the importance of developing techniques for digitally analyzing Android smartphones, which dominate the smartphone market. The primary objective of this research is to contribute to the development of effective forensic investigation strategies tailored specifically for Android mobile devices, providing insights into the tools and methods used for this purpose. The objective of this study is to improve the precision and effectiveness of forensic examinations pertaining to Android mobile phones. It discusses the fundamental functionality of mobile devices as a source of digital evidence and provides an overview of tools and methodologies for collecting and analyzing such evidence. The importance of comprehending the hardware and software architecture of Android handsets in order to choose the right forensic tools is also highlighted in the article. Furthermore, it proposes future enhancements for Andriller, a popular digital forensic tool, to improve its effectiveness in Android forensic investigations. These enhancements include advancements in data extraction techniques, compatibility with new Android versions, support for additional data types, integration with advanced analysis methods, and addressing identified limitations. Additionally, the paper stresses the need for robust methodologies for conducting cloud forensics on Android devices, particularly in the context of data stored in cloud storage services. The proposed work aims to enhance the capabilities of Andriller and improve the efficiency of digital forensic investigations on Android devices. © 2024 Elsevier B.V.. All rights reserved.

关键词： Android (operating system)

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Elevating Industrial Picker Arm through software Mechanic Fusion

Elevating Industrial Picker Arm through Software Mechanic Fu...

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international conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Rupali S Patil Aniket Apraj Sahil Bhosale Srushti Borikar Anushka Chauhan Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth College of Engineering Belpada Navi Mumbai Maharashtra India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

The manual operations that are performed in logistic warehouses like the separation of packages from clusters and their placement in the appropriate drop zones require immense human effort. Existing large production facilities use robotic arms for segregation, but because they can only pick up packages from predefined positions and drop them in designated zones, they are unable to manage packages that are stacked randomly. Our project presents an industrial robotic arm with sophisticated weight and dimension based sensing capabilities in order to get over this restriction. This robotic arm locates desired products in clusters that are randomly placed and drops them precisely in the right spots on the conveyor belt. The arm, which uses computer vision and machine learning algorithms, provides a workable way to expedite the process of segregation in a variety of industries, such as food and beverage, medical, and more. Customizing the arm to meet industry requirements could result in a significant increase in production and efficiency while lowering the need for manual labor.

关键词： Industries Mechatronics machine learning algorithms Service robots Manipulators Robot sensing systems software Trajectory Robots Logistics

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Research on the Application of Binary Classification Dataset Based on Integrated learning Model

Research on the Application of Binary Classification Dataset...

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Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Jiaqi Huang Chongyang Zheng Guohuan Feng Artificial Intelligence School Wuchang University of Technology Wuhan China Software College Northeastern University Shenyang China School of Engineering and Computer Science Oakland University Auburn Hills US

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The binary classification problem is a fundamental and core problem type in machine learning, and many machine learning algorithms, such as logistic regression and tree models, are widely used to solve binary classification problems. Based on the Kaggle open-source binary classification dataset, a prediction model for binary classification data was established using five different machine learning algorithms, namely KNN, decision tree, gradient boosting tree, random forest, and extreme random decision tree integration. The parameters of the models were determined through relevant theoretical analysis cross-validation methods, and the test accuracy of the five integrated learning models were compared, and the experimental results showed that the test accuracy of the gradient boosting tree and random forest models reached 93.23%, which indicated that these two models had the best prediction performance in solving the binary classification problem. In addition, these two models also have high stability. Therefore, this study provides strong support to the field of machine learning and demonstrates the potential of gradient boosted trees and random forests in solving real-world problems.

关键词： Analytical models Logistic regression machine learning algorithms Accuracy Predictive models Nearest neighbor methods Boosting Stability analysis Decision trees Random forests

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E-Voting System Using Block Chain

E-Voting System Using Block Chain

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Artificial Intelligence and machine learning Applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： E. Jithendra Reddy D. Rohit Kumar T. Venket Babu Sathyabama University Chennai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Voting is a way of taking collective selections with the aid of a group of human beings. Your comment or opinion for the agreement. The final results of the referendum should have extreme effects. Integrity should be maintained in this kind of way that there is no difference fraud or fraud in balloting. This is very It is critical that the voting is honest and impartial. This electronic voting machine is designed to eliminate all possibilities of fraud and enhance the reliability and credibility of voting. This purpose consists of things, viz. Administrators and Senior (Users). When electorate log in the usage of their valid username and password, they could view the upcoming elections and look at the applicants contesting the elections. Users can test the effects after the election is over. The device additionally indicates how a lot a user’s likes have been shared. On the other hand, the administrator of the software can view the listing of contesting applicants, voter listing and voter list. Since it stores facts the use of a system lock, it’s far very at ease and can be effortlessly checked for symptoms of tampering and misuse.

关键词： Cloud computing Prototypes Reliability engineering software Fraud Servers Security

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Comparative machine learning Approaches to Identify the Rice Cultivars

Comparative Machine Learning Approaches to Identify the Rice...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Aparna Sarkar B. Madhavidevi Soham Nandi Ananya Mukherjee Mahendran Botlagunta VIT Bhopal University Sehore India Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

This research introduces an innovative machine learning (ML) approach to efficiently classify diverse rice varieties, addressing the challenges of identification in the context of the staple food's rich cultural and emotional significance globally. Six distinct rice types from various Indian regions were chosen for the dataset, captured through images processed with ImageJ software, measuring parameters like area, perimeter, and Feret's diameter. The ML model, employing Random Forest and Decision Trees through supervised learning, demonstrated enhanced accuracy via ensemble learning, with the Bagging Classifier achieving the highest accuracy of 86.9%. Thorough statistical analysis, visualization, and dataset editing contributed to robust model training. Evaluation metrics included precision, recall, F1 score, ROC/AVC, accuracy, and cross-validation accuracy. Beyond efficient rice variety classification, this research addresses the prevalent issue of fraudulent rice mixing, providing a valuable tool for quality assurance and authenticity in the market. The proposed ML model's success opens the possibilities for broader applications in agricultural product classification, reshaping approaches to food quality and safety assurance in the evolving intersection of technology and agriculture.

关键词： Training Visualization Accuracy Quality assurance Supervised learning software Ensemble learning

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