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1st International Conference for Women in computing, InCoWoCo 2024

作者： Niharika, Yamini Akhila, Thirumalaraju Vaishnavi, Vemula Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

This project investigates Python to study the Traveling Salesman Problem (TSP) and looks at five different algorithms that can be implemented: Brute Force, Greedy, Genetic, Dynamic Programming, and Divide and Conquer. Locating the shortest path that makes accurate stops in each city before returning to the origin city is the task assigned to the TSP. In order to identify the best solution, the Brute Force algorithm thoroughly looks through every combination of city sequences. The nearest unexplored city is chosen at each stage via the Greedy Algorithm, in contrast, which makes locally optimal decisions. By applying genetic operators like crossover and mutation, genetic algorithms generate new generations of possible paths by iteratively improving existing ones, mimicking the principles of natural selection. To cut down on unnecessary computations, dynamic programming divides the problem into smaller subproblems and stores the answers. © 2024 IEEE.

关键词： Genetic algorithms

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Comparative Analysis of Machine Learning Models for Water Quality Prediction 4

Comparative Analysis of Machine Learning Models for Water Qu...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Krushna, Bode Vamsi Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Water Quality plays an essential role in assessing environmental health, which has profound implications for both ecosystems and human well-being. In this work, various machine learning models including Logistic Regression, Decision Trees, Gaussian Naive Bayes, Random Forest, Support Vector Machine (SVM), and XGBoost were employed to analyze and predict water quality. The dataset used for the analysis encompasses diverse water quality parameters such as hardness, pH levels, solids, chloramines, turbidity, sulfate among others. Class imbalances in the dataset is addressed with Synthetic Minority Over-sampling Technique(SMOTE). The research findings highlight the significant improvement in classification accuracy of underrepresented data points due to SMOTE, with SVM and XGBoost achieving a training accuracy of around 78% and 75%, respectively. The study used K-fold cross-validation techniques and strategies to handle convergence warnings, enhancing the credibility and adaptability of the predictions. This study not only contributes to the growing body of knowledge in water quality analysis but also provides practical insights for researchers and practitioners seeking to employ machine learning approaches in environmental monitoring, water contamination. These findings highlight the critical role of machine learning in facilitating informed decision-making for long-term water management, ensuring the health and longevity of communities and ecosystems. © 2024 IEEE.

关键词： Decision Trees Gaussian Naive Bayes Logistic Regression Machine learning Random Forest SVM Water quality management XGBoost

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Optimizing Smart Contract Security: A Cost-Sensitive Graph Neural Network Approach for Vulnerability Detection

Optimizing Smart Contract Security: A Cost-Sensitive Graph N...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Aravind, M. Saravanan, R. Saranya, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

This paper addresses the issues of blockchain platforms, especially Ethereum, smart contract vulnerabilities have resulted in significant monetary losses. The primary objective of this research is to identify critical smart contract vulnerabilities, such as reentrancy, overflow, and access control problems, by utilizing Graph Neural Networks (GNNs). To evaluate the degree to which two GNN-based models work at reducing practical financial losses, two of them are created using various training approaches. The first model treats each vulnerability class equally in an effort to maximize accuracy. The second model takes a more cost-effective tack by giving training greater weight to major weaknesses. We investigate the trade-offs between accuracy and financial risk reduction using a dataset of 2,217 contracts with imbalanced class distribution, replicating real-world settings. Financial loss estimates are derived from industry-reported incidents, and confusion matrices are employed to demonstrate the detection patterns. The results show that even though the first model's overall accuracy is higher at 96%, it endures more financial loss as a result of missing vital vulnerabilities. With a 94% accuracy rate, on the other hand, the cost-sensitive model effectively lowers high-impact false negatives, resulting in a less overall financial loss. Given that even a slight drop in accuracy might lead to more successful financial risk mitigation, our findings emphasize the significance of prioritizing vulnerability identification based on possible real-world repercussions. The groundwork for incorporating cost-sensitive models into smart contract auditing tools to improve blockchain security is provided by this research. © 2024 IEEE.

关键词： Graph neural networks

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Enhancing Software Test Effort Estimation using Ensemble Learning Algorithms 4

Enhancing Software Test Effort Estimation using Ensemble Lea...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Venkatraman, K. Akashvarma, M. Siddharth, S. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350349900

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Data delivery delay and cross-layer packet size analysis for reliable transmission of Licklider transmission protocol in space networks

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science China(Information sciences) 2024年第9期67卷 229-244页

作者： Guannan YANG Ruhai WANG Kanglian ZHAO Wenfeng LI Dong YAN School of Information Engineering Nanjing University of Finance and Economics Drayer Department of Electrical and Computer Engineering Lamar University School of Electronic Science and Engineering Nanjing University Beijing Aircraft Overall Design Department

Delay/disruption tolerant networking(DTN) is proposed as a networking architecture to overcome challenging space communication characteristics for reliable data transmission service in presence of long propagation delays and/or lengthy link disruptions. Bundle protocol(BP) and Licklider Transmission Protocol(LTP) are the main key technologies for DTN. LTP red transmission offers a reliable transmission mechanism for space networks. One of the key metrics used to measure the performance of LTP in space applications is the end-to-end data delivery delay, which is influenced by factors such as the quality of spatial channels and the size of cross-layer packets. In this paper, an end-to-end reliable data delivery delay model of LTP red transmission is proposed using a roulette wheel algorithm, and the roulette wheel algorithm is more in line with the typical random characteristics in space networks. The proposed models are validated through real data transmission experiments on a semi-physical testing platform. Furthermore, the impact of cross-layer packet size on the performance of LTP reliable transmission is analyzed, with a focus on bundle size, block size, and segment size. The analysis and study results presented in this paper offer valuable contributions towards enhancing the reliability of LTP transmission in space communication scenarios.

关键词： Licklider transmission protocol DTN bundle protocol cross-layer packets size space communication

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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud computing

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IEEE Access 2025年 13卷 92816-92832页

作者： Slathia, Ashwin Singh Sharma, Abhiram Krishna, P.B. Anand, Saksham Rathi, Ayush Joseph, Linda Gao, Xiao-Zhi Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India University of Eastern Finland School of Computing Kuopio Finland

Cloud computing plays a crucial role in modern technology, providing scalable and on-demand computing resources. However, excessive resource use can result in higher energy demand, higher operating expenses, and a more significant adverse effect on the environment as per study by A. Berl et al. [1]. In order to forecast and maximize the energy efficiency in cloud systems, this paper presents a machine learning based methodology. Using the Cloud Efficiency Dataset from Google Cloud, key performance indicators such as CPU usage, memory consumption, network traffic, and power consumption were analyzed. Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. Among them Random Forest model performed the best, achieving an RMSE of 0.16 and an accuracy of 96.8 %. To improve the interpretability of the model Explainable Artificial Intelligence (XAI) techniques were applied, specifically SHapley Additive exPlanations (SHAP), to evaluate feature importance. The findings demonstrated that the most important variables which influence energy efficiency are CPU usage and memory consumption. This research develops A novel algorithm based on the XAI results. Unlike static thresholding or heuristic schedulers, our SHAP-driven approach dynamically adapts VMplacement decisions with built-in interpretability. It is capable to efficiently schedule and allocateVirtual Machines on the cloud in a live environment. The proposed framework demonstrates tthe high scalability and real time applicability, making it suitable for the deployment in live cloud environments. While the integration of XAI methods like SHAP enhances interpretability, it introduces additional computational overhead—a trade-off that is justified by the improved transparency and decision making support. © 2013 IEEE.

关键词： Resource allocation

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Gesture Recognition Technology in Smart Gloves Enhanced by Machine Learning 4

Gesture Recognition Technology in Smart Gloves Enhanced by M...

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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Thangam, S. Kandi, Nithin Charan, Dorai Sai Gosu, Vijay Kumari, J. Jesy Janet Virupaxappa, G. Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529635

Gesture recognition systems are changing how people interact with devices, promising natural and intuitive hands-free control in various applications. Based on the ESP32 microcontroller and machine learning techniques, the proposed work is developing a compact, power-efficient system for recognizing hand gestures from data gathered by flex sensors and accelerometers. The entire system fits within memory constraints, as lightweight machine learning models are first trained on a computer using Python and then ideally deployed in optimized versions onto the ESP32. In this respect, the stream of pre-processed sensor data happens to be fed to the ML model for inference, sending back results to the ESP32, which hence triggers specific actions or commands. This fundamentally new approach shows the feasibility of machine learning on resource-constrained platforms, opening the road for wearable technologies that will be more clever and reactive. © 2024 IEEE.

关键词： Accelerometers

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Parsing Ambiguous Grammar-A Survey 15

Parsing Ambiguous Grammar-A Survey

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sowmith, Jetty Dasari, Lalith Abhiram Mamidi, Naveen Krishna Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This survey is aimed at showing different ways of, how some linguists try to get rid of the ambiguity feature of the syntax, but there are some or no techniques for checking whether the given phrase is ambiguous or not. The chief purpose of most software quality analysis methods is the same and it is to get an abstract model from the source code, which one can use for the abstraction process or further analysis. Translating abstract representation or the extension came out through a structured mechanism called parsing. Often, the set of grammar rules governing the structure of the parser is language-specific, thus enabling the translation of source code written in one specific language into the defined abstract tree form structure. This work highlights the main approaches used to remove ambiguity from ambiguous grammar. © 2024 IEEE.

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Automatic feature selection and classification for Apple Disease Prediction using CNN 4

Automatic feature selection and classification for Apple Dis...

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4th International Conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Uchimuthu, Muthaiah Ramanathan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349856

The four most prevalent diseases that affect apple leaves are black rot, cedar rust, scab and leaf blotch. The spread of infection can be stopped and the healthful growth of the apple sector ensured by early detection and correct identification of apple leaf diseases. The Convolutional Neural Network (CNN) achieved a commendable accuracy of 89.42%. In comparison, the utilization of Inception v3 demonstrated a substantial enhancement, yielding an impressive accuracy of 92.50%. These results underscore the superiority of Inception v3 in accurately classifying apple leaf diseases, marking a significant advancement in predictive modeling for agricultural applications. The result is more superiority of MobileNet in classifying apple leaf disease more accurately with accuracy of 96.88%. In comparison of all three algorithms, MobileNet has more accuracy to offer a valuable tool for precise disease detection and informed decision-making for farmers, ultimately contributing to enhanced agricultural productivity and sustainable farming practices. © 2024 IEEE.

关键词： Decision making

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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