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International Conference on High Performance Computing

作者： Weicong Chen Curtis Tatsuoka Xiaoyi Lu Dept. of Computer and Data Sciences Case Western Reserve University Cleveland USA Dept. of Medicine University of Pittsburgh Pittsburgh USA Dept. of Computer Science and Engineering University of California Merced Merced USA

The COVID-19 pandemic has necessitated disease surveillance using group testing. Novel Bayesian methods using lattice models were proposed, which offer substantial improvements in group testing efficiency by precisely quantifying uncertainty in diagnoses, acknowledging varying individual risk and dilution effects, and guiding optimally convergent sequential pooled test selections. Computationally, however, Bayesian group testing poses considerable challenges as computational complexity grows exponentially with sample size. HPC and big data stacks are needed for assessing computational and statistical performance across fluctuating prevalence levels at large scales. Here, we study how to design and optimize critical computational components of Bayesian group testing, including lattice model representation, test selection algorithms, and statistical analysis schemes, under the context of parallel computing. To realize this, we propose a high-performance Bayesian group testing framework named HiBGT, based on Apache Spark, which systematically explores the design space of Bayesian group testing and provides comprehensive heuristics on how to achieve high-performance, highly scalable Bayesian group testing. We show that HiBGT can perform large-scale test selections (> 2 50 state iterations) and accelerate statistical analyzes up to 15.9x (up to 363x with little trade-offs) through a varied selection of sophisticated parallel computing techniques while achieving near linear scalability using up to 924 CPU cores.

关键词： COVID-19 Statistical analysis Computational modeling Scalability Lattices Life estimation Parallel processing

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ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

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International Conference on Circuits, Communication, Control and Computing

作者： Pradeep Kumar D Sowmya BJ Anita Kanavalli Annvin Vincent Dept. of Computer Science and Engineering M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore Dept. of Artificial Intelligence and Data Science M S Ramaiah Institute of Technology (Affliaited to VTU) Bangalore

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed.

关键词： Benchmark testing Artificial bee colony algorithm Whale optimization algorithms Hybrid power systems Optimization Standards Convergence

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A Decision Support Framework for Sustainable Waste Disposal Technology Selection

A Decision Support Framework for Sustainable Waste Disposal ...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Venkateswara Reddy Lakkireddy R. Madana Mohana B. Rama Ganesh Lakkireddy Udanth Reddy Saikat Gochhait Shrish Chogle Department of Computer Science and Engineering (DS) KG Reddy College of Engineering and Technology Hyderabad India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering Sri Venkatesa Perumal College of Engineering and Technology Tirupathi India Dept. of Electronics and Communication Engineering VIT AP University Amaravathi India Symbiosis Institute of Digital and Telecom Management constituent of Symbiosis International Deemed University

Waste Disposal Technology (WDT) selection is a primary issue in Municipal Solid Waste (MSW) that affects the development of the environmental and economic perspectives/aspects, particularly in developing countries. The selection of appropriate WDT is a complex Multi-Attribute Decision-Making (MADM) problem with both qualitative and quantitative elements. The existent MADM approaches with fuzzy sets (removal of uncertainty), different subjective weight methods (significance of attributes), and rank reversal phenomenon leads to improper selection of WDT due to the involvement of different opinions of decision-makers. To avoid this, a Decision Support Framework (DSF) was proposed for optimal WDT selection for the growth of economic and environmental development. The proposed DSF integrates Preference Selection Index (PSI) and a Modified-Comprehensive distance Based Ranking (M-COBRA) approaches to determine the significance of attributes and ranking the alternatives, respectively. The DSF is illustrated using a case study collected from Iran and compared with state-of-the-art MADM approaches. Further, the DSF is validated in terms of sensitivity analysis, rank reversal phenomenon, and Pearson's rank correlation coefficient to ensure the stability of ranking.

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FDM: NutriGuide - A Cross-Platform Flutter Application for Managing and Educating on Nutritional Deficiencies

FDM: NutriGuide - A Cross-Platform Flutter Application for M...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Vidhya K T.M Thiyagu Antony Taurshia B. Nagarajan Jenefa A Joshua Flower Jebas I School. of CST Karunya Institute of Tech. and Sci. Coimbatore India Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Sci. and Tech. Dept. of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

In today's fast-paced world, many individuals strug-gle to maintain a balanced diet, leading to various nutritional de-ficiencies. Effective management and education on these deficien-cies are crucial for improving public health. Traditional methods of addressing nutritional deficiencies often lack personalization and user engagement. The NutriGuide application, developed using the Flutter framework, aims to revolutionize nutritional management by providing a user-centric, cross-platform solution. Utilizing a comprehensive database of nutrients and symptoms, the application offers personalized dietary recommendations based on user inputs. The dataset includes detailed information on common deficiencies, symptoms, and nutrient-rich foods. Experimental results indicate high user satisfaction, with an average satisfaction score of 4.5 out of 5, and significant im-provements in nutritional intake, such as a 28.6 % increase in Vitamin A intake and a 50% increase in Vitamin D intake. In conclusion, NutriGuide presents an innovative approach to nutritional health, combining modern technology with evidence-based recommendations to empower users in managing their dietary needs.

关键词： databases Education Nutrients Electronic healthcare Internet of Things Public healthcare

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Leveraging Deep Belief Networks and Multi-Class Support Vector Machine for Nutritional Deficiency Identification in Grape Plant Leaves

Leveraging Deep Belief Networks and Multi-Class Support Vect...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： P Kiran N Saikiran D Srilatha K Venkateswara Rao K M Rayudu Rudrapati Mounika Dept. of CSE (AI) Madanapalle Institute of Technology & Science (A) Madanapalle Andhra Pradesh India Dept. of CST Madanapalle Institute of Technology & Science (A) Madanapalle Andhra Pradesh India Department of EEE Vasi Reddy Venkatadri Institute of Technology Namburu India Department of CSE Vignan's Lara Institute of Technology & Science Vadlamudi India Dept. of Data Science QIS College of Engineering and Technology Ongole India Department of CSE Malla Reddy Engineering College (A) Maisammaguda Telangana India

Modern fertilization includes identifying nutritional deficits in plant leaves since soils frequently fall short of providing growing crops with all the nutrients the earth needs. The creation of a fertilization program to deliver the desired micronutrients while not overproducing others will be made easier by determining the amount of vitamins and minerals that are required. The indications of agricultural malnutrition are nutritional insufficiency indicators in plant leaves, which may be physically examined from the plant morphological elements and so offer adequate information to identify the nutritional deficit. This study was intended to focus on nutritional deficiency identification in grape plant leaves. In this work, Deep Belief networks (DBNs) are employed for training the model. The output of the model is fed to a Multi-Class Support Vector Machine (SVM) to classify the grape plant leaf images as healthy, N-deficiency, P-Deficiency, K-Deficiency, Ca-Deficiency, Mg-Deficiency and S-Deficiency. Accuracy and Misclassification rate is used to analyze the performance of the model. The results indicated that the suggested model achieved an accuracy of 96.8% and a misclassification rate of 3.2%. These results show that DBNs are a potential technology for nutritional deficiency categorization in grape plant leaf tissue.

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Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT

arXiv

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arXiv 2024年

作者： Khatun, Mirza Akhi Eising, Ciarán Bhattacharya, Mangolika Dhirani, Lubna Luxmi Dept. Electronic & Computer Engineering SFI CRT Foundations in Data Science University of Limerick LimerickV94 T9PX Ireland Dept. Electronic & Computer Engineering University of Limerick LimerickV94 T9PX Ireland School of Information Technology Illinois State University Illinois United States

This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92% accuracy in identifying possible attacks. © 2024, CC BY.

关键词： Convolutional neural networks

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Multidimensional Impacts of Generative AI and an In-dept. Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K Ramu Ginni Nijhawan G Lalitha Praveen V Asha Ahmed J Fakher Dept. of CSE Velammal Institute of Technology Chennai TN India Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Applications New Horizon College of Engineering Bangalore Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous.

关键词： Surveys Performance evaluation Ethics Privacy Generative AI Reviews Large language models

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Equivariant Diffusion for Crystal Structure Prediction 41

Equivariant Diffusion for Crystal Structure Prediction

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41st International Conference on Machine Learning, ICML 2024

作者： Lin, Peijia Chen, Pin Jiao, Rui Mo, Qing Cen, Jianhuan Huang, Wenbing Liu, Yang Huang, Dan Lu, Yutong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Supercomputer Center Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China Institute for AIR Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP. Copyright 2024 by the author(s)

关键词： Crystal symmetry

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Optimizing V2V Communication in 5G/6G Networks with Enhanced Deep Cooperative Q-Learning

Optimizing V2V Communication in 5G/6G Networks with Enhanced...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Priyameet Kaur Keer N. Lakshmi Deepthi Ginni Nijhawan Neeraj Chahuan Ali Albawi Madhu Palati Master of Business Administration New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Dept. of EEE BMS Institute of Technology and Management Bangalore

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Understanding the physical channel characteristics of V2V communications is important, but it is neither cost-effective nor practical to have full information about them. Since there is always delay and data-loss during communications, which are further accelerated when the wireless channel status and device parameters remain uncalibrated frequently, full or even partial information of the environment is not feasible, even for a short period. Moreover, the vehicular mobility can also introduce abrupt changes to communication. These uncertainties dictate that a system should be well-and adaptively-designed to handle changing information. V2V communications that exploit this information naturally serve as candidates for artificial Intelligence (AI) algorithms. Specifically, there are applications of cooperative learning strategies that are suitable, including machine learning, deep reinforcement learning, and deep multi-agent reinforcement learning. This paper applies novel Enhanced Deep Cooperative Q-Learning (DCO-DQN) model for V2V communication, to achieve the best trade-offs (latency/ reliability/safety) under various states, including short and long memory, asymptotic and non-asymptotic, possible and unpredictable outcomes scenarios. The proposed framework incorporates spatial wisdom and historical information into Euclidean spaces, accounting for the above behavior and challenges. For this reason, future V2V communication in 5G/6G networks may benefit from adopting our approach, as previously described. A thorough comparative study has been conducted to illustrate our advantages and shortcomings. Our analysis also offers insights on hybrid techniques and decentralized approaches, as possible means for future development.

关键词： Wireless communication 6G mobile communication Q-learning Uncertainty 5G mobile communication Vehicular ad hoc networks Interference Linear programming Safety Optimization

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Predictive Maintenance in Aerospace Industry Using Convolutional Neural Network

Predictive Maintenance in Aerospace Industry Using Convoluti...

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International Conference on Mechatronics (ICOM)

作者： Dwi Pebrianti Zulhakim Khalani Rusdah Luhur Bayuaji Dept. of Mechanical & Aerospace Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia Faculty of Data Science & Information Technology INTI International University Nilai Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Predictive maintenance offers many benefits compared to reactive and preventive maintenance. Reactive maintenance is often cosidered too late due to its characteristic to fix failure after the failures happened and preventive maintenance is considered too early in maintenance process due to its characteristic to substitute spareparts far before the failure happens. The benefit of predictive maintenance includes enhanced safety, reduced downtime, improved staff planning, reduced operational costs, enhanced asset management and improved regulatory compliances. This paper presents an approach of machine learning in predictive maintenance to improve the current corrective maintenance used in the aviation industry. The goal of this study is to develop a predictive failure model using Convolutional Neural Network (CNN) to provide an early detection of engine breakdown before its occurrence. The data used in the study is the data of the aircraft engine operation cycle measured by 21 sensors. The prediction of failure is made on the Remaining Useful Life (RUL) of the engine. The failure prediction results will be compared with Recurrent Neural Network (RNN) at the end of this paper to see whether the proposed approach could outperform the existing method being applied or not. The results showed that CNN achieved greater accuracy, precision, recall, and F1-score compared to Recurrent neural network (RNN) in failure prediction. The result shows that the proposed method accurately predicts the failures in the system with 98% of accuracy, 96% of precision and 96% of Recall. In conclusion, this study demonstrates that employing a Convolutional Neural Network (CNN) for predictive maintenance in the aviation industry significantly enhances failure prediction accuracy, thereby contributing to improved safety and operational efficiency.

关键词： Industries Accuracy Recurrent neural networks Atmospheric modeling NASA Predictive models Maintenance

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