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International Symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Development of Interpretable Machine Learning Models for COV...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqi Shi Mio Murakoso Xiaoyan Guo May D. Wang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Life Science Kyoto University Kyoto Japan College of Computing Georgia Institute of Technology Atlanta USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta USA

With the extensive time and financial requirements incumbent on drug discovery, computational approaches, such as protein-ligand docking predictions, are increasingly crucial for accelerating the process of drug repurposing. However, the proliferation of identified protein targets has exposed a critical knowledge gap in developing robust models that offer both generalizability and interpretability for docking score prediction. Addressing this, our study presents a machine learning-based surrogate model, employing interpretable artificial intelligence techniques for accurate docking score prediction for SARS-CoV-2 protein targets. We demonstrate the model generalization on its expansion to accommodate unseen protein targets by integrating protein target information through feature concatenation. Moreover, we leverage the SHapley Additive exPlanations (SHAP) method to identify the data-driven feature importance of molecular substructures for knowledge-based validation. Our experiments reveal that the combination of data-driven prediction and knowledge-driven validation could provide biomedical insights into the interactions between drugs and SARS-CoV-2 proteins, elucidating their consequent effects on docking scores.

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AI-Driven CASGN Framework for Evaluating Descriptive Answers in Online Education

AI-Driven CASGN Framework for Evaluating Descriptive Answers...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sridevi Ponmalar. P Vinodini S V. Srithar Gouri Morankar S D Sundarsingh Jebaseelan Harikrishnan. R Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai Faculty of Arts (English) Sathyabama Institute of Science and Technology Chennai Department of Computer Applications PeriyarManiammai Institute of Science & Technology Thanjavur Shri Ramdeobaba College of Engineering and Management Nagpur Department of EEE Sathyabama Institute of Science and Technology Chennai Symbiosis Institute of Technology Pune Campus Symbiosis International Deemed University Pune

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

There is an enormous demand for automated assessment systems, which can handle the bulk assessments efficiently and effectively. While existing systems perform well in grading objective questions, they break down with subjective or descriptive answers that require a deeper analysis of context and understanding. To address this challenge, we have proposed the AI-Enhanced Context-Aware Semantic Grading Network-state-of-the-art algorithm for instant auto-descriptive response scoring in e-assessments. The AI-CASGN utilizes state-of-the-art AI technologies, particularly NLP and deep learning models-very specifically transformer architectures-during context-based semantic analysis. Beyond mere keyword matching, the system scores what students say and how relevant it is. This takes the concept of grading to an even more fine-tuned art. Scores change with depth and accuracy in the answer given to show complete evaluation. With the dynamic grading framework, adaptation on scores both regarding the accuracy and depth of the answer for a precise evaluation is possible. Inbuilt adaptive learning within the system performance evolves day by day by learning from the new data. AI-CASGN is designed to handle biases in mitigation by considering that there may be different patterns for a response to ensure that responses with varied wording but conceptually correct are fairly graded. It will have to deal with the volumes at scale, hence it is envisaged to process large volumes of responses in minimum time and with high accuracy to be deployed in real time in any educational institution. In future, the system will also provide personalized feedback to students for the purpose of continuous learning and improvement. This represents a scalable, real-time, unbiased tool that assesses subjective answers in modern online learning environments.

关键词： Deep learning Adaptive learning Accuracy System performance Prevention and mitigation Semantics Transformers Real-time systems Natural language processing Context modeling

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach

Enhancing Lung Disease Identification with Multimodal Data F...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： T. Grace Shalini G. Susan Shiny R. Saranya P Suresh Babu R Kavitha Athiraja Atheeswaran Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of EEE Velammal college of engineering and technology Madurai India Department Of Computer Science And Engineering Bannari Amman Institute Of Technology Sathyamangalam Erode India Department of information technology Sethu Institute of Technology Kariapatti India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of CSE (AIML) Malla Reddy College of Engineering Hyderabad India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an $F 1$-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine.

关键词： Deep learning Pathology Image analysis Pulmonary diseases Soft sensors Lung Data integration Medical services Convolutional neural networks Medical diagnostic imaging

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LIFEGPT: TOPOLOGY-AGNOSTIC GENERATIVE PRETRAINED TRANSFORMER MODEL FOR CELLULAR AUTOMATA

arXiv

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arXiv 2024年

作者： Berkovich, Jaime A. Buehler, Markus J. Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Civil and Environmental Engineering Department of Mechanical Engineering Center for Computational Science and Engineering Schwarzmann College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

Conway’s Game of Life (Life), a well known algorithm within the broader class of cellular automata (CA), exhibits complex emergent dynamics, with extreme sensitivity to initial conditions. Modeling and predicting such intricate behavior without explicit knowledge of the system’s underlying topology presents a significant challenge, motivating the development of algorithms that can generalize across various grid configurations and boundary conditions. We develop a decoder-only generative pretrained transformer (GPT) model to solve this problem, showing that our model can simulate Life on a toroidal grid with no prior knowledge on the size of the grid, or its periodic boundary conditions (LifeGPT). LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. We also introduce the idea of an ‘autoregressive autoregressor’ to recursively implement Life using LifeGPT. Our results pave the path towards true universal computation within a large language model framework, synthesizing of mathematical analysis with natural language processing, and probing AI systems for situational awareness about the evolution of such algorithms without ever having to compute them. Similar GPTs could potentially solve inverse problems in multicellular self-assembly by extracting CA-compatible rulesets from real-world biological systems to create new predictive models, which would have significant consequences for the fields of bioinspired materials, tissue engineering, and architected materials design. © 2024, CC BY-NC-ND.

关键词： Inverse problems

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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DPA-2:a large atomic model as a multitask learner

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npj computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Predicting Event Participation for an Event-Based Social Network

Predicting Event Participation for an Event-Based Social Net...

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IEEE International technology Management Conference

作者： Akshay Seedath Nicholas Hosein Patrick Hosein College of Computing and Engineering Georgia Institute of Technology Atlanta USA CEO VENZI Fort Lauderdale USA Department of Computer Science The University of the West Indies St. Augustine Trinidad

ISBN: (数字)9798350367355

ISBN: (纸本)9798350367362

Accurately predicting event attendance is essential for organizers to allocate resources, improve user experience, and ensure success. This study examines user engagement within the Venzi event-based social networking app by analyzing chat interactions, event views, and user applications and approvals. Key factors influencing attendance and turnout rate-the percentage of approved users who attend-are identified and used to build predictive models. We compare the performance of linear regression, XGBoost, and a specialized weighted distance model (Kappa). The analysis highlights the significance of user interaction metrics in forecasting event outcomes and provides actionable insights for organizers to enhance engagement strategies and optimize attendance.

关键词： Measurement technology management Social networking (online) Event detection Linear regression Predictive models User experience Forecasting

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

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International Conference on computational and Characterization Techniques in engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning Data preprocessing Reinforcement learning Feature extraction Data models Predictive analytics Stress

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： R. Usharani V. M. Sivagami K. Saravanan S. Pushparani K. Sashi Rekha Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Chennai Tamil Nadu Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations.

关键词： Cloud computing Technological innovation Costs Biological system modeling Machine learning Predictive models Maintenance

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