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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sri Harsha, M.V.R. Akhil, M.H.S. Ronald Doni, A. Albert Mayan, J. Yovan Felix, A. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9798350349221

This study, detailed below, introduces a new method of handling crimes in particular states, using ensemble machine learning and data visualization techniques in particular, as a way to enhance public safety. Crime detection is a hard task for two concerns, the complexities of the crime and the enormity of the data. By means of random forest and AdaBoost ensemble learning techniques we can model sophisticated problems of the relationship between features such as crime data as a way to match predictions. The other point we stress is data visualization which allows us to comprehend the patterns and connections that have been derived from the ensemble learning process. In addition to the analysis of local crime data, the hybrid approach that we propose involves the inclusion of national crime data to make the identification of hidden crime patterns at state level possible. This move will enable the law enforcement agencies to gain a better understanding of the activities and to effectively respond to the crimes. The proposed methodology represents a, robust and efficient solution to statewise crime detection utilizing the advantages of machine learning and mapping tools. © 2024 IEEE.

关键词： Crime

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Real-Time Network Monitoring: A Big Data Approach 14

Real-Time Network Monitoring: A Big Data Approach

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

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SN computer science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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Advanced Machine Learning Models for Electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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Predicting Customer Lifetime Value in E-commerce: A Data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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A Novel Approach for Tomato Quality Evaluation: computer Vision in Maturity Assessment and Disease Detection 5

A Novel Approach for Tomato Quality Evaluation: Computer Vis...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Arya, Prarthana K.Y. Kulkarni, Tejas P. Pushpa, B.R. Rani, N. Shobha Sangamesha, M.A. Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysore India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Chemistry National Institute of Engineering Mysore India

ISBN: (纸本)9798350361155

Tomatoes (Solanum lycopersicum) are one of the most widely consumed fruits globally. Fruit maturity and disease estimation is a criticalfactors in determining its quality and marketability. Traditional maturity assessment requires huge labor, slow, subjective, and time-consuming activities. To address these challenges, the agriculture industry is subjected to automation using computer vision models. Accurate prediction of tomato maturity and diseases is important to decide on harvesting time. Several features such as color, shape, texture, and size are considered to predict the different maturity stages of fruits using deep learning and machine learning models. Assessing the maturity level and disease detection in the early stage of tomatoes is crucial. The proposed work explores the application of the VGG16 architecturefor tomato maturity classification and disease detection. The study leverages transfer learning with VGG16focusing on feature extraction and model optimization to achieve accurate classification. The datasets used in this work are self-created using the images that are collected from agricultural farms. The dataset consists of image samples of six different maturity stagesand diseased fruit images. The experiment results in an accuracy range between 95%-99% for six maturity stages of tomatoes and disease prediction resulted in 90%-97% accuracy. © 2024 IEEE.

关键词： Fruits

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Data driven approach to identify a flow-based Botnet Host using Deep Learning

Data driven approach to identify a flow-based Botnet Host us...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Mishra, Aniket Bharathi, Indira School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350396485

The internet's technological advancements exposed the globe to its weaknesses as well. The risk of exploitation has also increased as a result of larger network cores cooperating to combat cyber threats, which continue to be a severe problem for the entire world. Recurrent Neural Networks (RNN)-based deep learning techniques have recently advanced to new levels in a variety of fields and applications. The risk of forged accounts is greater than ever thanks to increased network use and traffic. The challenge to identify a malicious host on the internet has always been a challenge from the development perspective. The job of binary classification to label a host as a botnet has not made any significant progress and thus still, the internet faces the issue of botnets taking over many active and important connections exploiting the network, controlling compromised hosts to spam other hosts on the network, launch DDoS attacks and more. This paper attempts to provide a novel approach for evolving the comprehensive framework for controlling botnet host prediction and uses them to handle real time cases. To attain greater recognition accuracy, we use Gated Recurrent Unit (GRU) as a hybrid Recurrent Neural Network (RNN) model. We take an evolving time series input from a network station for several days which depicts data flow i.e., count of connections from different devices recognized by their IPs, and these features are used from the IP flow to provide capability to recognize the host on a network as a potential threat. Threat detection of such botnets is important not only from the perspective of stopping them but also to find significant insights about the targeted attack to understand future trends and make the networks persistent against them. © 2023 IEEE.

关键词： Time series

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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