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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Ahilya Chhetri Sumukh Surya Mohan Krishna S Vidya Rao Engineering and Technology Cactus Communications Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Department of Electrical and Electronics Engineering ACED Alliance University Bengaluru India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz; lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation.

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UNWIND - A Mobile Application that Provides Emotional Support for Working Women 2

UNWIND - A Mobile Application that Provides Emotional Suppor...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Kugapriya, Priyanka Manohara, Mayuriya Ranganathan, Keerthiga Kanapathy, Dineshgaran Gamage, Anjalie Anzar, Arshad Sri Lanka Institue of Information Technology Data Science Malabe Sri Lanka Sri Lanka Institue of Information Technology Software Engineering Malabe Sri Lanka Sri Lanka Institue of Information Technology Information Technology Malabe Sri Lanka Sri Lanka Institue of Information Technology Department of Information Technology Malabe Sri Lanka SprigHub

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Depression is a common phenomenon affecting more than 264 million people worldwide. It is one of the leading causes of disability and a major contributor to the overall global burden of disease. Around twice as many women are affected by mental illness compared to men. This situation has worsened during the pandemic. The need to balance both work life and personal life has put them under immense pressure. Even though the diagnosis of mental illness almost exclusively depends on doctor-patient communication, it has its own set of disadvantages such as patient denial, recall bias, subjective biases, time-consuming and inaccuracy and it is a long-term health problem that needs to be continuously monitored and managed. Considering this social problem, we have planned to develop an Emotional Support Mobile application UNWIND - using modern technological concepts of machine learning and artificial intelligence. Which focuses especially on working women and would include several functionalities: a Chabot to detect mental health status in real-time and to provide counseling, an internal activities tracker to find the correlation between changes in lifestyle and mental health, an improvement tracker of the user's current mental state using facial recognition and also Recommendation system with the support group, which recommends the most suitable professional counselors to the user as per their preferences and enabling into the support group to provide with necessary treatments and consultation at greater accuracy. © 2022 IEEE.

关键词： Diseases

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

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Information Technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods Information technology Random forests Bagging Logistics

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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IEEE International Conference on Big data

作者： Mohammad Masum Nafisa Anjum Md Jobair Hossain Faruk Hossain Shahriar Nazmus Sakib Maria Valero M.A. Karim Akond Rahman Fan Wu Alfredo Cuzzocrea Department of Applied Data Science San Jose State University San Jose USA Department of Information Technology Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Civil and Environmental Engineering Kennesaw State University USA Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480468

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country’s social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country’s 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2 nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score.

关键词： Bridges Decision making Transportation Machine learning Maintenance engineering Predictive models Big data

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Quantum Machine Learning for software Supply Chain Attacks: How Far Can We Go?

Quantum Machine Learning for Software Supply Chain Attacks: ...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Mohammad Masum Mohammad Nazim Md Jobair Hossain Faruk Hossain Shahriar Maria Valero Md Abdullah Hafiz Khan Gias Uddin Shabir Barzanjeh Erhan Saglamyurek Akond Rahman Sheikh Iqbal Ahamed Analytics and Data Science Institute Kennesaw State University USA Department of Computer Science Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department Information Technology Kennesaw State University USA Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University USA Department of Computer Science Marquette University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.

关键词： Computers Machine learning algorithms Supply chains Neural networks Qubit software algorithms Support vector machine classification

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoqi Hua Jian Chen Peiyun Zhang Zhangjie Fu Haibin Zhu Kezhong Lu Jigang Ren School of Software Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics of Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Mathematics Nipissing University North Bay ON Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

关键词： Throughput Blockchains Peer-to-peer computing Delays Synchronization Cybernetics

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Cloud computing-based higher education platforms during the COVID-19 pandemic

arXiv

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arXiv 2022年

作者： Han, Hui Trimi, Silvana Data Science Department Fraunhofer Institute For Experimental Software Engineering IESE Kaiserslautern Germany Department Of Supply Chain Management And Analytics College Of Business University Of Nebraska - Lincoln Lincoln United States

Cloud computing has become the infrastructure that supports people's daily activities, business operations, and education delivery around the world. Cloud computing-based education platforms have been widely applied to assist online teaching during the COVID-19 pandemic. This paper examines the impact and importance of cloud computing in remote learning and education. This study conducted multiple-case analyses of 22 online platforms of higher education in Chinese universities during the epidemic. A comparative analysis of the 22 platforms revealed that they applied different cloud computing models and tools based on their unique requirements and needs. The study results provide strategic insights to higher education institutions regarding effective approaches to applying cloud computing-based platforms for remote education, especially during crisis situations. © 2022, CC BY-NC-SA.

关键词： Cloud computing

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Prostate Cancer data Analytics Using Hybrid ECNN and ERNN Techniques 1

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1st International Conference on Business data Analytics, ICBDA 2022

作者： Srinivasulu, Asadi Gupta, Anand Kumar Kolambakar, Swapnil B. Subramanyam, Madhusudana Rajeyyagari, Siva Ram Barua, Tarkeshwar Pushpa, Asadi BlueCrest University Monrovia1000 Liberia Data Science Research Lab BlueCrest University Monrovia1000 Liberia OpenLabs BlueCrest University Monrovia1000 Liberia Department of CS and Engineering KL University Andhra Pradesh Guntur India Department of CS and IT Shaqra University Shaqra Saudi Arabia REGex Software Services Jaipur India ECE Department S.V University A.P. Tirupati India

ISBN: (数字)9783031236471

ISBN: (纸本)9783031236464

Prostate is a subsequent driving reasons for malignant growth passings among male human. Primal location of malignant tumor can actually lessen the pace of deathrate brought about by Prostatic malignant growth. Because of high and multi-resolution of MRIs from prostatic malignant growth require a legitimate demonstrative frameworks and instruments. In the foregone specialists created Computer supported finding (CAD) frameworks that assist the radiologist with distinguishing the irregularities. In this exploration paper, we have utilized novel Deep Learning methods like Hybrid ECNN-ERNN. Convolutional Neural Networks and Recurrent Neural Networks for distinguishing prostate cancer. In addition, various elements extricating techniques are proposed to further develop the location execution. The highlights removing methodologies depend on surface, morphological, scale invariant element change (SIFT), and elliptic Fourier descriptors (EFDs) highlights. The exhibition was assessed in light of single as well as mix of elements utilizing Deep Learning Classification strategies. The Cross approval (Jack-blade k-crease) was performed and execution was assessed in term of get working bend (ROC) and particularity, responsiveness, Positive prescient worth (PPV), negative prescient worth (NPV), misleading positive rate (FPR). In view of single highlights separating methodologies, ECNN procedure gives the most elevated precision of 98.34% with AUC of 0.999. While, utilizing mix of highlights removing procedures, ECNN with surface + morphological, and EFDs + morphological elements give the most noteworthy precision of 99.71% and AUC of 1.00. The proposed model outperformed than the existing system with parameters / metrics as accuracy (82.42%), error rate (0.13), val_loss (0.41), val_accuracy (0.50), size of dataset used in research (1.50 GB), No. of epochs (30), Time-complexity (O(n2)) and execution time (1022 ms). © 2022, The Author(s), under exclusive license to Springer Nature

关键词： Convolutional neural networks

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CLDG: Contrastive Learning on Dynamic Graphs

CLDG: Contrastive Learning on Dynamic Graphs

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International Conference on data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG.

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