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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Deshmukh, Vaishnavi J Ambhaikar, Asha Kalinga University Department of Computer Science & Engineering Chattisgarh Raipur India Kalinga University Faculty of Engineering Department of Computer Science and Engineering Chattisgarh Raipur India

ISBN: (纸本)9798350348460

In the context of online news outlets like Twitter, our work gives a summary of the situation as of rumor identification utilizing visual content. The majority of studies in the literature use visual content to illustrate several solutions to the issue. As opposed to combining them, we suggested an approach for Twitter rumours. The majority of our research work focuses on the use of supervised techniques, particularly deep learning models by utilising the deduced feature sets from the Twitter MediaEval dataset, which included 3000 images in total, a VGG-19 deep neural network was trained and evaluated. The VGG-19 achieves a highest sensitivity and an accuracy of using features taken from images during training and testing, which struggle to function in situations other than those in which they were trained. The simulation findings show that compared to other state-of-the-art architecture, the suggested design is more accurate and sensitive. © 2024 IEEE.

关键词： deep learning machine learning Twitter VGG-19

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Co-clustering method for cold start issue in collaborative filtering movie recommender system

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Multimedia Tools and Applications 2024年 1-25页

作者： AbbasiRad, Ensieh Keyvanpour, Mohammad Reza Tohidi, Nasim Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Computer Engineering Faculty K.N.Toosi University of Technology Tehran Iran Computer Science & amp Engineering University of Michigan Ann ArborMI United States

Recommender systems play an essential role in decision-making in the information age by reducing information overload via retrieving the most relevant information in various applications. They also present great opportunities and challenges for business, government, education, and other fields. The cold start problem is a significant issue in these systems. If recommender systems fail to provide satisfactory personalized recommendations for new users, the user’s trust can easily be lost. Hence, in this paper, using co-clustering and utilizing user demographic information and the behavioral history of users, a solution to this critical issue for recommending movies is introduced. In the proposed method, in addition to dealing with the problem of relative cold start, the problem of absolute cold start is also addressed. The proposed method was evaluated via two RMSE and MAE criteria, which accordingly has achieved 0.85 and 0.49 on the Movielens dataset and 1.05 and 0.6 on the EachMovie dataset, respectively, according to the number of comments that Cold Start users have registered. Moreover, it achieved 0.9 and 0.55 on the Movielens dataset and 1.42 and 0.89 on the EachMovie dataset, respectively, according to the number of registered comments for the cold start items. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Gender-Based Analysis of User Reactions to Facebook Posts

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Big Data Mining and Analytics 2024年第1期7卷 75-86页

作者： Yassine El Moudene Jaafar Idrais Rida El Abassi Abderrahim Sabour Mathematical Engineering and Computer Science Laboratory Faculty of SciencesIbn Zohr UniversityAgadir 8706Morocco Department of Computer Science Mathematical−High School of Technology Ibn Zohr UniversityAgadir 8706Morocco

Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the *** diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual *** paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion *** of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?

关键词： profiling knowledge extraction data mining emotion mining social media data crawling Facebook reactions gender

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Tech-Enabled Altruism: Harnessing Technology in Social Media for Food Waste Reduction 3

Tech-Enabled Altruism: Harnessing Technology in Social Media...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sujatha, E. Jayanthi, M. Sandeepa, N. Raju, D. Naveen Nagaraj, S. Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India R. M. K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518592

Altruistic Connections is a pioneering social media platform that amalgamates the functionalities of popular networking applications with a unique emphasis on fostering altruistic endeavors. Drawing inspiration from established platforms like Instagram, this project introduces a distinct dimension by enabling users not only to share their stories and experiences but also to engage in philanthropic activities seamlessly. The platform facilitates direct communication through messaging, creating a communitydriven space where users can connect over shared values and altruistic initiatives. Users can post, like, save, and comment on content while also exploring and participating in a marketplace specifically designed for charitablecontributions. The technical architecture of 'Altruistic Connections' ensures a secure and user-friendly experience. Robust messaging features, seamless posting mechanisms, and an integrated altruistic marketplace form the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. Strategies for user acquisition involve collaborations with philanthropic influencers, engagement with altruistic communities, and partnerships to expand the platform's reach. The project's financial sustainability is achieved through potential collaborations with philanthropic organizations and businesses that align with the spirit of altruism. Addressing potential challenges related to the verification of charitable activities, transaction security, and content moderation, the platform implements rigorous measures to create a trustworthy and fulfilling environment. The abstract concludes by emphasizing the commitment of 'Altruistic Connections' to redefine social media engagement through technology, altruism, and the power of community. An altruistic marketplace forms the core of the platform. Emphasis is placed on privacy and data security, aligning with the trust users place in the community. © 2024

关键词： Marketplaces

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Highly efficient ternary organic solar cells with excellent open-circuit voltage and fill factor via precisely tuning molecular stacking and morphology

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science China Chemistry 2024年第3期67卷 963-972页

作者： Dinghong Sun Zhenyu Chen Jianfeng Zhang Wei Song Jingyu Shi Jintao Zhu Yuanyuan Meng Fei Jin Shuncheng Yang Ziyi Ge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Faculty of Material Science and Chemical Engineering Ningbo UniversityNingbo 315211China Zhejiang Provincial Engineering Research Center of Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049China Department of Chemical and Environmental Engineering University of Nottingham NingboNingbo 315100China

The micro-morphology and molecular stacking play a key role in determining the charge transport process and nonradiative energy loss, thus impacting the performances of organic solar cells(OSCs). To address this issue, a non-fullerene acceptor PhC6-IC-F with alkylbenzene side-chain, possessing optimized molecular stacking, complementary absorption spectra and forming a cascade energy level alignment in the PM6:BTP-eC9 blend, is introduced as guest acceptor to improve efficiency of ternary OSCs. The bulky phenyl in the side-chain can regulate crystallinity and optimizing phase separation between receptors in ternary blend films, resulting in the optimal phase separations in the ternary films. As a result, high efficiencies of 18.33% as photovoltaic layer are obtained for PhC6-IC-F-based ternary devices with excellent fill factor(FF) of 78.92%. Impressively, the ternary system produces a significantly improved open circuit voltage(V_(oc)) of 0.857 V compared with the binary device,contributing to the reduced density of trap states and suppressed non-radiative recombination result in lower energy loss. This work demonstrates an effective approach for adjusting the aggregation, molecular packing and fine phase separation morphology to increase V_(oc) and FF, paving the way toward high-efficiency OSCs.

关键词： alkylbenzene side-chain morphology regulation ternary blends organic solar cells

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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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A Variational Inference-Based LSTM-enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Ines Raissa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala8580 Cameroon Department of computer engineering University of Buea Faculty of Engineering and Technology Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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