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Deep Bimodal Fusion Approach for Apparent Personality Analysis

computers, Materials & Continua 2023年第4期75卷 2301-2312页

作者： Saman Riaz Ali Arshad Shahab S.Band Amir Mosavi Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Computer Science Institute of Space TechnologyIslamabad44000Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliu64002YunlinTaiwan Faculty of Civil Engineering Technische Universitat DresdenDresden01069Germany

Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks.

关键词： Apparent personality analysis deep bimodal fusion convolutional neural network long short-term memory bimodal information fusion approach

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Morpho-contour exponential estimation algorithm for predicting breast tumor growth from MRI imagery

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Kaladevi, P. Kanimozhi, N. Nirmala, B. Sivasankari, R. Department of Computer Science and Engineering K. S. Rangasamy College of Technology (Autonomous) Tamil Nadu Tiruchengode 637215 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai 603203 India Computer Science and Engineering Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Porur Chennai 600116 India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Chennai 600 048 India

Breast tumor grows rapidly have an elevated risk of progressing to a metastatic state, leading to the spread of tumor tissues to other regions of the body. Early detection of such metastatic sites can be facilitated by predicting these tumors' growth rate. In this context, we introduce a novel Morpho-Contour Exponential Estimation (MoCEE) method to estimate the potential growth rate of identified breast tumors using magnetic resonance imaging (MRI) datasets. This methodology not only effectively captures morphological traces but also pinpoints regions indicative of infectious spread. This precision is achieved by enhancing the Mask R-CNN with an active contour function, ensuring the accurate segmentation of the breast tumor. Once segmented, the tumor's growth rate is computed using the exponential growth model, which accurately determines the rate by analyzing the distance between two distinct random points on the tumor's segmented region. Subsequently, this derived information, processed through a gradient boosting approach, predicts the likelihood of tumor growth across two different breast tumor categories. In our study, we assessed the proposed MoCEE method from two crucial angles. Firstly, we rigorously evaluated its ability to accurately segment both benign and malignant breast tumors. Our analysis involved comparing MoCEE's performance to that of various deep learning networks, yielding impressive segmentation accuracy percentages, such as 93.12% and 92.22% for benign and malignant tumors in BUSnet, and even higher values for other networks. Secondly, we explored MoCEE's capability to estimate tumor size, considering different probability growth rates (k^=0.2, 0.3, 0.4, and 0.5). Notably, proposed MoCEE proved highly accurate in predicting tumor growth rates across this range of probabilities. These findings highlight MoCEE's versatility and robustness, making it a valuable tool for precise breast tumor analysis and prediction. © The Author(s), under exclusiv

关键词： And gradient boosting approach Breast tumors Exponential growth model Mask R-CNN Morpho-contour exponential estimation (MoCEE)

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Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning 13th

Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesia...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Ouyang, Hui Chen, Cheng Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have become increasingly vital in representing complex stochastic processes in various domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is a challenging endeavor, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Bayesian Networks (BNs), and are typically focused on smaller-scale problems. In order to solve large-scale problems while taking full advantage of existing algorithms, this paper introduces a novel divide-and-conquer strategy, originally developed for static BNs, and adapts it for large-scale DBN structure learning. Additionally, we leverage the prior knowledge of 2 Time-sliced BNs (2-TBNs), a special class of DBNs, to enhance the performance of this strategy. Our approach significantly improves the scalability and accuracy of 2-TBN structure learning. Designed experiments demonstrate the effectiveness of our method, showing substantial improvements over existing algorithms in both computational efficiency and structure learning accuracy. In problem instances with more than 1,000 variables, our proposed approach on average improves two accuracy metrics by 74.45% and 110.94%, respectively, while reducing runtime by an average of 93.65%. Moreover, in problem instances with more than 10,000 variables, our proposed approach successfully completed the task in a matter of hours, whereas the baseline algorithm failed to produce a reasonable result within a one-day runtime limit. © IFIP International Federation for Information Processing 2024.

关键词： Bayesian networks

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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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DTQFL: A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network

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IEEE Journal of Biomedical and Health Informatics 2023年第5期29卷 1-10页

作者： Qu, Zhiguo Li, Yang Liu, Bo Gupta, Deepak Tiwari, Prayag Engineering Research Center of Digital Foren-sics Ministry of Education Jiangsu Collaborative Innovation Center of Atmospheric Environment the Equipment Technology 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 Hubei University of Science and Technology Xianning China Department of Computer Science & Engi-neering Maharaja Agrasen Institute of Technology Delhi India School of Information Technology Halmstad University Sweden

Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training. IEEE

关键词： Wireless networks

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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G Communication Environment

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Tsinghua science and technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Amit Vajpayee Palak Preet Kaur Ankit Sharma Santosh Varshney Department of Computer Science and Engineering Parul Institute of Engineering and Technology Vadodara India Department of Bigdata Analytics Apex Institute of Technology Chandigarh University Mohali India Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making.

关键词： Technological innovation Machine learning algorithms Power demand Neuromorphic engineering Reviews Machine learning Big Data History Biological neural networks Faces

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A Review on the Role of Blockchain technology for Decentralized Identity Management: A Future Without Passwords 4

A Review on the Role of Blockchain Technology for Decentrali...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Raut, Rohini Gourshettiwar, Palash Thakre, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board. © 2025 IEEE.

关键词： Decentralized finance

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Power and Area Optimized Memristor-Based Majority Identifier for Digital Applications 3

Power and Area Optimized Memristor-Based Majority Identifier...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Raj, P. Michael Preetam Vittapu, Sravan K. Chinnasamy, P. Gupta, Manu Samatha, K. Sasivijaya, K. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Electronics and Communication Engineering Hyderabad India Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798331518592

In earlier developments, CMOS technology played a great role in realizing majority identifiers. There are huge drawbacks that affect the circuit's performance. To overcome the drawbacks of CMOS-based majority Identifiers, novel memristor-based majority and minority Identifiers have been proposed in this work. The features offered by memristor have motivated us to use the component and achieve greatly improved results compared to CMOS-based majority identifier circuits. After observing the behavior of the basic memristor-based majority identifier circuit, we have decided to design and implement a memristor-based majority and minority identifier circuit which can be used in many automation systems to increase quality and productivity. © 2024 IEEE.

关键词： CMOS integrated circuits

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Unveiling the Root Cause of EV Charging Irregularities: A Statistical Approach 1

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International Conference on Green Energy, Computing, and Intelligent technology, GEn-CITy 2023

作者： Bajaj, Ankit Gopalani, Dinesh Mathur, Rachit Reddy, Hemanjaneya Satyanarayan, Swapna Chand, Ansuman Department of Computer Science Engineering Malaviya National Institute of Technology Jaipur India Mercedes-Benz Research and Development Bengaluru India

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The drive for mobility has caused an increase in greenhouse gas emissions, leading to a shift toward the adoption of electric vehicles (EVs). These vehicles are powered by efficient electric motors, which offer reduced upkeep and enhanced operation. However, the unstable nature of electricity demand and the proliferation of EV charging equipment providers have posed significant challenges in EV charging. Furthermore, charging interruptions are commonplace, with electrical vehicle supply equipment (EVSE) serving as a mysterious black box for EV owners. By analyzing EVSE and EV data during charging, sensitive signals of charging termination can be discovered. The present research paper aims to investigate endurance data to uncover the root cause of EV charging interruption and to address uncertainties in EV charging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Greenhouse gases

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