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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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Bug Prioritization Using Average One Dependence Estimator

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Intelligent Automation & Soft computing 2023年第6期36卷 3517-3533页

作者： Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi Department of IT and Computer Science Institute of Applied Sciences and TechnologyPakAustriaFochhshuleHaripurPakistan Faculty of Computer Sciences and Information Technology Superior UniversityLahore54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper DirKhyber PakhtunkhwaPakitan School of Computing Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120Korea

Automation software need to be continuously updated by addressing software bugs contained in their ***,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and *** managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical ***,bug report prioritization is *** study pro-poses a new model for bug prioritization based on average one dependence estimator;it prioritizes bug reports based on severity,which is determined by the number of *** more the number of attributes,the more the *** proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation *** of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)*** and Mozilla datasetswere used as the sources of bug *** proposed model improved the bug repository management and out-performed the SVM and NB ***,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.

关键词： Bug report triaging prioritization support vector machine Naive Bayes

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Urban Slum Mapping Using Homogeneous Urban Patches 3

Urban Slum Mapping Using Homogeneous Urban Patches

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Prabhu, R. Priya, M. Sangeetha Dayana, R. Suresh, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Campus Chennai India Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Sriperumbudur Chennai India R.M.D. Engineering College Kavaraipettai Department of Mathematics Thiruvallur India

ISBN: (纸本)9798350361186

Slums (Informal Settlements) are the most dynamic areas whose geographical locations are inaccurate according to official statistics and maps because of the exponential expansion of urbanization strategies. As a result, a method has to be developed to distinguish between informal settlements and track their spatial patterns. This paper presents an integrated method for detecting slums from satellite images that employs homogeneous urban patches (HUP) and grey level co-occurrence matrix (GLCM)-based image segmentation. In order to extract meaningful spatial features, urban patches are segmented according to their homogeneity and GLCM analysis provides useful texture information about slum areas. The combination of both these GLCM and HUP features allows for the creation of a classification map that accurately identifies and maps slum areas. We utilized WorldView-2 (1.84m) satellite imagery of Madurai to test the effectiveness of proposed HUP approach. The validation process includes rigorous testing against ground truth data, with careful consideration given to the selection of training and testing datasets. The effectiveness of the proposed method is evaluated using metrics including overall accuracy, user accuracy, and producer accuracy Furthermore, the proposed algorithm outperformed existing GLCM-based approaches in terms of accuracy. © 2024 IEEE.

关键词： Image segmentation

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Marine Ecosystem Monitoring: Applying Remote Sensing and AI to Track and Predict Coral Reef Health

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 486-499页

作者： Veeranjaneyulu, Rayavarapu Govindarajan, Dinesh Subramanian, Chandramohan Devi, Deva Uma Banerjee, Sudipta Edpuganti, Sai Krishna Upadhyay, Shrikant Department of Computer Science & amp Engineering PACE Institute of Technology and Sciences Andhra Pradesh Ongole India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of ECE SCSVMV Deemed University Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering (A) Andhra Pradhesh Visakhapatnam India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur Dist. India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

Coral reefs are essential ecosystems, supporting a diverse range of marine life and offering considerable ecological, economic, and cultural benefits. However, they face increasing threats from climate change, pollution, and other human activities. Thus, effective monitoring and management of coral reefs are crucial for their conservation and sustainability. In this study, we employed a hybrid model, HCNN-SVM, which combines convolutional neural networks (CNNs) for feature extraction and support vector machines (SVMs) for classification. We utilized a coral-reef dataset from Kaggle, containing images from CoralNet and the Moorea Coral Reef Long Term Ecological research (MCR LTER). To enhance image quality and ensure accurate reflectance values, we applied radiometric, geometric, and water column corrections during preprocessing. Feature extraction involved the use of spectral indices such as the Coral Bleaching Index (CBI) and Normalized Difference Vegetation Index (NDVI) to detect healthy and stressed corals, along with texture analysis to differentiate substrates. The model was trained on this diverse dataset, which captures various environmental conditions and reef types, achieving an accuracy of 98.55%. Cross-validation methods were employed to evaluate model performance, ensuring robustness and generalizability. The HCNN-SVM model demonstrated high accuracy in identifying and mapping coral reef components, making it a powerful tool for monitoring coral reef health and supporting conservation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks (CNNs) Coral Bleaching Index (CBI) Coral reef monitoring Feature extraction HCNN-SVM Moorea Coral Reef Long Term Ecological research (MCR LTER) Normalized Difference Vegetation Index (NDVI) Support vector machines (SVMs)

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Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning

Smart Surveillance System and Prediction of Abnormal Activit...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Gnanavel, S. Duraimurugan, N. Jaeyalakshmi, M. Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai6021053 India

ISBN: (纸本)9789819967544

Although surveillance cameras are used in ATM cells, we face some problems of robbery and theft at ATMs due to lack of security;however, the monitoring capacity of law enforcement agencies has not kept pace. ATM spoofing attacks can be carried out to break or damage the ATM by stealing the machine and taking cash from the ATM. To reduce this problem, we arm the ATM with a camera module mounted in the room to perform continuous video observation. The camera detects the human and his activity in the ATM and attempts to breach the ATM. It detects unusual activities and immediately sends an alert notification to the police. Therefore, the system handles the application developed to automate video surveillance and detect any potential criminal activity at ATMs. Therefore, in this work, abnormal behavior is observed using CNN and RNN in surveillance videos. These algorithms can be used to recognize faces, detect and track camera movements, and detect and identify the action required to prevent such activity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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A machine learning-based framework to Classify X-rays anomality 5

A machine learning-based framework to Classify X-rays anomal...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Saranya, V.S. Kanagajothi, D. Rajina Begam, S. Sharma, Sunil Dubey, Nilesh Rehan, Sumaiya School of Computing Department of Computing Technologies Srm Institute of Science and Technology Kattankulathur Chennai India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of science and technology Avadi. India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan university Tamil Nadu Trichy India Department of Computer Science and Engineering Vivekananda global University Jaipur India Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research FTE CHARUSAT University India Department of Computer Science and Engineering Babu Banarasi Das University Lucknow India

ISBN: (纸本)9798400709418

In recent years, the intersection of healthcare and technology has witnessed transformative advancements, particularly in medical imaging. The integration of machine learning techniques has empowered the medical community to enhance diagnostic accuracy and streamline healthcare processes. This paper introduces a comprehensive machine learning-based framework designed for classifying X-ray anomalies within the dynamic and scalable environment of cloud computing. The motivation behind this framework stems from the need for scalable, accurate, and cost-effective solutions in healthcare. Leveraging cloud computing's elastic resources, our framework aims to accelerate the deployment of machine learning models for X-ray anomaly classification, ensuring accessibility and affordability across diverse healthcare settings. Gather a diverse dataset of X-ray images with both normal and abnormal cases. Ensure a balanced representation of different anomalies. Utilize the validation set to fine-tune hyperparameters and prevent overfitting. Develop a robust machine learning model capable of accurately classifying X-ray anomalies, leveraging advanced convolutional neural networks (CNNs) and transfer learning techniques. The hardware and software required are discussed in relation to the functioning and integration of Cloud computing and Blockchain. Then we have gone through the model of machine learning for detection of anomality in X-rays(infection) step by step and in detail. The model discussed in the research paper can be of great importance to society as many people in society lose their lives to pneumonia every year. With the help of image classification, we can detect the areas of lung infected with infection, hence helping doctors in swift treatment of the patients. © 2023 ACM.

关键词： Cloud computing

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Optimizing Security Through Advanced Algorithms for Networked Infrastructure 2

Optimizing Security Through Advanced Algorithms for Networke...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prakash Jayant, Khel Kumar Singh, Abhishek Thapliyal, Nitin Sharma, Prashant Goyal, Sunita Raj Kumar Goel Institute of Technology Computer Sciences & Engineering UP Ghaziabad India Shiva Institute of Management Studies Department of Computer Science UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Civil Engineering UP Mathura India Abes Engineering College Department of Applied Science & Humanities UP Ghaziabad India

ISBN: (纸本)9798350371055

Contemporary networks are complex structures that require superior algorithms to maintain their security. This paper explores the usage of superior algorithms to cozy networks, focusing on the improvement and deployment of efficient and comfortable algorithms for networked infrastructure. It examines current algorithms used to cozy networks, together with authentication and encryption algorithms, as well as a spread of rising technologies, which include artificial intelligence (AI) and gadget learning (ML). it'll study techniques to optimize the safety of the network through the improvement of smart protection architectures, as well as the demanding situations and opportunities posed by real-international implementations paper will offer a evaluation of various safety algorithms utilized in networks, and speak strategies for efficient deployment and integration in current networks. It'll define demanding situations related to the deployment of advanced algorithms on networks and provide a top level view of the modern nation of safety solutions and their implications for networked infrastructure. Sooner or later, it's going to offer guidelines for destiny studies to further optimize safety for allotted infrastructure. Networked Infrastructure security is a fundamental part of any contemporary gadget. To make certain that such systems are thoroughly covered from malicious actors, superior algorithms are frequently employed to optimize the overall safety of the community. These algorithms are used to identify and detect malicious activities, as well as offer anomaly detection competencies. Moreover, they frequently leverage synthetic intelligence techniques along with device gaining knowledge of and herbal language processing to carry out extra effective and accurate protection analysis. The usage of those algorithms can ensure complete security coverage, as well as improve reaction time to threats and vulnerabilities. Furthermore, it is able to provide an extra degree

关键词： algorithms deployment maximize networked protection

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Enhancement of mechanical properties in AA5083 aluminum metal matrix composites through alumina and titanium carbide reinforcement

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Interactions 2024年第1期245卷 1-12页

作者： Vadivel, M. S., Seenivasan P., Satishkumar S., Gomkale R., Saminathan Department of Civil Engineering Nehru Institute of Technology Tamilnadu Coimbatore India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Applied Chemistry Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

As a result of its remarkable resistance to corrosion, strong wear resistance, high strength, and low weight, aluminum metal matrix composites (AMMC’s) have garnered tremendous widespread recognition for structural and aeronautical applications. Several mechanical properties of aluminum alloy (AA5083) are examined in this experimental study, which focuses on the impact of reinforcing particles (alumina and titanium carbide). The stir casting method was used to manufacture AMMC. Fabrication was carried out using the less difficult ex-situ approach. Reinforcement particle weight percentages ranging from 4 to 12% were used in the MMC preparation process. Machine operations were performed on the ingots following manufacturing in order to prepare test samples. To investigate the mechanical and tribological properties and effects of different reinforcement weight percentages, tests were conducted including tensile, compression, impact, hardness and wear tests. Adding alumina and titanium carbide resulted in a gradual rise of the tensile and compression strengths. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Aluminum corrosion

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Adversarial Threats in Machine Learning: A Critical Analysis

Adversarial Threats in Machine Learning: A Critical Analysis

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2024 International Conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Chahar, Suman Gupta, Sonali Dhingra, Isha Kaswan, Kuldeep Singh University Institute Of Computing Chandigarh University Mohali India Graphic Era Hills University Department Of Computer Science And Engineering Dehradun India Graphic Era Deemend To Be University Dehradun India Galgotias University Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9798350306446

The fast advancement of machine learning (ML) techniques has brought about both innovative opportunities and unprecedented security challenges. In this paper, we consider the some important field of machine learning conduct a critical survey and analysis of machine language security and its importance, attacks and threats, aiming to provide a comprehensive understanding of the evolving landscape in cybersecurity. We begin by elucidating the fundamental concepts and terminologies associated with ML attacks, laying the groundwork for an in-depth exploration. Subsequently, we categorize and dissect various types of ML attacks, including adversarial attacks, data poisoning, model inversion, and membership inference, among others. Real-world case studies and examples across different domains, such as computer vision and natural language processing, are scrutinized to reveal the intricacies and implications of these attacks. Furthermore, we evaluate existing defense mechanisms and countermeasures, assessing their effectiveness against different attack scenarios. Finally, we discuss the broader societal impacts and ethical considerations surrounding the deployment of ML technologies in security-critical applications. this paper provides the tools to handle the machine language's attacks and their solution using integration of academic research with real world examples. © 2024 IEEE.

关键词： Cybersecurity

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Loan Prediction Using Machine Learning

Loan Prediction Using Machine Learning

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sarisa, Hemanth Kumar Khurana, Varun Koti, Venkat Chandu Garg, Neha Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering India

ISBN: (纸本)9798350344387

Loan prediction is a significant problem in the banking industry. Using Technology, will change and make some improvements in Loan prediction by bank employees manually. By using "Machine learning "in loan prediction. The machine learning system will check the data for a loan of the applicant and the data will be checked so fast and without any human error. So, it will help to improve technology in Bank sectors. By this, processing the data will be checked quickly, making the data output in less time. It helps both the applicant and the bank *** learning algorithms can be used to analyze historical data and make accurate predictions about whether a loan application will be approved or not. In this study, we propose a machine learning-based loan prediction model that utilizes various features such as credit score, income, loan amount, and loan term. We explore different classification algorithms, including logistic regression, decision tree, and random forest, to build the loan prediction model. Our experimental results show that the random forest algorithm outperforms other models with an accuracy rate of 83.45%. The proposed model can help banks and financial institutions streamline their loan approval process, reduce manual effort, and minimize the risk of bad loans. © 2023 IEEE.

关键词： Data Pre-Processing Decision Tree K-Neighbours Classifier Machine Learning Python Random Forest Classifier

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