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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Geetha, B.T. Kafila Ram, S Tulasi Amar Prabhakar Narkhede, Dr. Bani Ahmad, Ahmad Y. A. Tiwari, Mohit Saveetha School of Engineering Simats Department of Ece Tamil Nadu India Sr University School of Business Telangana Warangal India Chaitanya Bharathi Institute of Technology School of Management Studies Hyderabad India Isms Sankalp Business School Vadgon Pune India Middle East University Faculty of Business Department of Accounting and Finance Amman11831 Jordan Applied Science Private University Applied Science Research Center Jordan Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350384277

Using blockchain technology to build strong digital asset management tools for financial operations is a revolutionary way to bring financial systems up to date. Blockchain technology is changing the way money works by making transfers decentralised, open, and safe. Digital Asset Management (DAM) systems help businesses easily store and share digital assets like cryptocurrencies and blockchain-based assets. Using blockchain tools like smart contracts and tokenization makes financial deals safer and more trustworthy by using cryptographic encryption and ledger systems that can't be changed. Streamlining processes and payment makes financial operations more efficient. But problems like scaling and following the rules must be dealt with. Blockchain technology has many uses in finance, such as cross-border payments, tokenizing assets, and smart contracts. It is also changing how financial services are regulated. This study uses secondary data from academic research papers, peer-reviewed journals, and industry reports to look the use of blockchain technology to financial transactions, learn about DAM basics, look into blockchain tools in financial services, and judge how blockchain is used in financial transactions. This study helps us understand and use strong digital asset management systems in financial transactions that use blockchain technology by doing a thorough analysis. © 2024 IEEE.

关键词： Decentralized finance

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Prioritizing TWAS Genetic Biomarkers for Melanoma Metastasis via Gene Expression Profiling*

Prioritizing TWAS Genetic Biomarkers for Melanoma Metastasis...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Mohamed N. Saad Belal Medhat Mohamed Hamed Biomedical Engineering Department Faculty of Engineering Minia University Egypt Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA) Rostock University Medical Center Rostock Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Egypt Faculty of Media Engineering and Technology German University in Cairo Egypt

The development of novel therapeutic approaches that target gene alterations in melanoma cells remains an area of active research. This study prioritized ten genes out of 76 genes - associated with melanoma through a transcriptome-wide association study (TWAS). We investigated the potential of the identified ten genes in prognosis of metastasis in melanoma *** Relevance: Our findings have clinical implications for melanoma treatment strategies. Targeted therapies that specifically address the dysregulation of our 10 genes may hold promise in preventing or slowing melanoma metastasis.

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Sentence Embedding Models for Similarity Detection of Software Requirements

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SN computer Science 2021年第2期2卷 1-11页

作者： Das, Souvick Deb, Novarun Cortesi, Agostino Chaki, Nabendu Department of Computer Science and Engineering University of Calcutta Kolkata 700106 India Indian Institute of Information Technology Vadodara Gujarat Gandhinagar 382028 India Department of Environmental Science Informatics and Statistics Ca’ Foscari University Venezia 30172 Italy Department. of Computer Science and Engineering University of Calcutta Kolkata 700106 India

Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sentence embedding models trained on a natural language requirements dataset in order to derive sentence embeddings specific to the software requirements engineering domain. We use cosine-similarity measures in both these models. The result of the experimental evaluation confirm that the proposed models enhance the performance of textual semantic similarity measures over existing state-of-the-art neural sentence embedding models: we reach an accuracy of 88.35%—which improves by about 10% on existing benchmarks. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Requirements similarity Semantic similarity Sentence embedding

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A Multi-Stream Scrambling and DNA Encoding Method Based Image Encryption

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computer Systems Science & engineering 2023年第11期47卷 1321-1347页

作者： Nashat Salih Abdulkarim Alsandi Dilovan Asaad Zebari Adel Al-Zebari Falah Y.H.Ahmed Mazin Abed Mohammed Marwan Albahar Abdulaziz Ali Albahr Information Technology Duhok Private Technical InstituteDuhokIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Iraq Department of Information Technology Technical College of Informatics AkreDuhok Polytechnic UniversityDuhokIraq Faculty of Computing and Information Technology Sohar UniversityOman College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Science Umm Al Qura UniversityP.O.Box 715MeccaSaudi Arabia College of Applied Medical Sciences King Saud Bin Abdulaziz University for Health SciencesAl Ahsa31982Saudi Arabia King Abdullah International Medical Research Center Al-Ahsa36276Saudi Arabia

Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and ***,the progress of image encryption techniques is becoming an increasingly serious issue and considerable *** space of the key,encryption-based low confidentiality,low key sensitivity,and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this *** our proposed scheme,a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption *** the confusion stage,different scrambling operations related to the original plain image pixels are designed using Chen’s map.A stream pixel scrambling operation related to the plain image is ***,a block scrambling-based image encryption-related stream pixel scrambled image is *** the diffusion stage,two rounds of pixel diffusion are generated related to the confusing image for intra-image ***’s map,logistic map,and DNA computing are employed to construct diffusion operations.A reverse complementary rule is applied to obtain a new form of DNA.A Chen’s map is used to produce a pseudorandom DNA sequence,and then another DNA form is constructed from a reverse pseudorandom DNA ***,the XOR operation is performed multiple times to obtain the encrypted *** to the simulation of experiments and security analysis,this approach extends the key space,has great sensitivity,and is able to withstand various typical *** adequate encryption effect is achieved by the proposed algorithm,which can simultaneously decrease the correlation between adjacent pixels by making it near zero,also the information entropy is *** number of pixels changing rate(NPCR)and the unified average change intensity(UACI)both are very near to optimal values.

关键词： Grayscale image encryption stream scrambling-confusion DNA encoding XOR operation chaotic systems

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An Android App for Posture Analysis Using OWAS

An Android App for Posture Analysis Using OWAS

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16th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Lins, Christian Quang, Franziska Minh-Khai Dang Schulze, Rica Lins, Stefanie Hein, Andreas Fudickar, Sebastian Department of Computer Science Hamburg University of Applied Sciences Hamburg20099 Germany Assistance Systems and Medical Device Technology Carl von Ossietzky University Oldenburg Oldenburg26129 Germany MOVE Junior Research Group Institute of Medical Informatics University ofLuebeck Germany University of Hagen Hagen58097 Germany

In this paper the APA, an App for Posture Analysis, that incorporates the OWAS method (Ovako Working Posture Analysis System) for assessing potential harmful postures of physically hard working employees, is presented and evaluated. The app is intended as a tool for occupational safety experts in assessing the individual postures of workers to identify and prevent harmful working situations in regards to musculoskeletal hazards. For this, APA incorporates a digitized assessment sheet for the OWAS method and timing support that helps occupational safety experts structure and simplify the assessment workflow. To investigate whether the protocol sheet can be replaced with the app, a study was conducted in which the inter-rater reliability among the app and the paper protocol sheet (as control group) was evaluated. In addition, the usability of the app was determined via the User Experience Questionnaire (UEQ), In the study, the app achieved higher inter-rater reliability for watching a video recording of postures than the control group. The chi-square test revealed differences in the use of the app and paper only for leg postures. The UEQ indicated overall above average results, which indicates a sufficient usability, which was also confirmed by free-textual comments of the 13 study participants. The results suggest that it would be possible to replace the paper protocol sheet in the future with an app. © 2023 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Musculoskeletal system

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Advancements in Image Caption Bots: A Deep Learning Approach

Advancements in Image Caption Bots: A Deep Learning Approach

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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Adarsh Dabral Raj Gusain Sushant Shekhar Anurag Vidyarthi Rishi Prakash R. Gowri Department of Computer Science Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India National Center for Geodesy (NCG) Indian Institute of Technology Kanpur Kanpur India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

In recent years, artificial intelligence (AI) has changed the area of computer vision, and image captioning is one of the many applications that have benefited from these advancements. An image caption bot is an intelligent system that generates a textual description of an image. This research paper will explore the development of image caption bots, the underlying technologies, and the various techniques used to build these systems. Image caption bots are developed with the help of deep learning models (DLM) such as convolutional neural networks (CNNs) the most popular nowadays and recurrent neural networks (RNN s) which is also popular in researchers. Featureextraction has been done from the images with CNN sand RNN s are used to generate the captions. These deep-learning models have significantly improved the accuracy and performance of image captioning systems. During training, the CNN extracts the relevant features from the images, and the RNN generates the corresponding captions. In order to decrease the discrepancy between generated and ground truth captions, the model is trained. The accuracy and efficiency of image caption procedures could be greatly increased by using the image caption bots developed in this research. These systems have several applications and can greatly aid visually impaired individuals in understanding images, assist search engines to better index and search images, and improve the accessibility of social media platforms for users with disabilities. Further research in this field could lead to more accurate and comprehensive Image captioning systems.

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Towards Bi-level Supply/Demand Balanced Charging Systems via Online Power Scheduling

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IEEE Transactions on Mobile Computing 2025年

作者： Lu, Xinyu Lou, Jiong Li, Jie Xu, Runhui Wu, Chentao Liu, Zhi Luo, Yuan Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Yancheng Blockchain Research Institute China Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering China Shanghai Jiao Tong University Innovation Center of Intelligent Connected Electric Vehicle in Global Institute of Future Technology Shanghai200240 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connect supply and demand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On a macroscopic level, it impacts the power grid's peak load and reliability, while locally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2. © 2002-2012 IEEE.

关键词： Charging stations

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Constructing an Evaluation Framework for English Teaching in Higher Education: Integrating Neural Networks and Natural Language Processing 3rd

Constructing an Evaluation Framework for English Teaching in...

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Deshmukh, Ram Ramachandran, K. Anute, Nilesh Rajeswari, T.S. Chouhan, Kuldeep Fatma, Gulnaz School of Engineering SR University Telangana Warangal India Postdoctoral Researcher Jesselton University College Jalan Lintas Sabah Kota Kinabalu88300 Malaysia ASM’S Institute of Business Management and Research Pune India Department of English Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida201310 India Department of English Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789819727155

In the dynamic landscape of higher education, the effectiveness of English teaching is paramount, as it directly impacts students’ communication skills, academic success, and future career prospects. To enhance the quality of English instruction in higher education, this study presents an innovative evaluation model that harnesses the power of neural networks and natural language processing (NLP). The model's potential for customization and scalability makes it a valuable tool for higher education institutions seeking to optimize their English teaching programs. Furthermore, it contributes to the broader exploration of how AI and NLP technologies can transform the field of education, fostering more efficient and personalized learning experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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