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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Biswas, Praloy Neogi, Subhrendu Guha Arockiam, Daniel Mitra, Anirban Amity University Computer Science and Engineering Department Madhya Pradesh Gwalior India The Neotia University Computer Science and Engineering Department West Bengal Sarisha India Amity University Computer Science and Engineering Department West Bengal Kolkata India

ISBN: (纸本)9798350350357

multilingual sentiment analysis has become essential for brands targeting international customers across multiple cultures and languages as a result of the corporate world's growing globalization. To accurately comprehend client opinions and expectations, it is becoming increasingly important to analyze feelings expressed in local languages. In contrast to translating literature to English, it emphasizes how linguistic subtleties, contextual allusions, and cultural connotations collected through native language analysis offer greater insights. Key issues are listed in this research, including linguistic diversity, a dearth of training data, and challenges related to cultural context interpretation across geographic boundaries. To assist businesses in deriving useful insights from multilingual data, it highlights the necessity for advanced Artificial intelligence and deep neural network solutions that incorporate cross-channel, cross-lingual analytics frameworks. Brands can gain a major competitive advantage in optimizing global marketing strategies, increasing customer engagement, and fostering enduring loyalty by tracking attitudes in local languages. This paper proposed a novel approach to deal with multi-lingual Sentiment Analysis in Digital Marketing. © 2024 IEEE.

关键词： Sentiment analysis

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A GPS-based Face Attendance Register System using Android Applications stored in the Cloud 18

A GPS-based Face Attendance Register System using Android Ap...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Murali, N. Rajesh, R. Sridharan, S. Mariadas, A. Emmanuel Peo Murugan, S. Palani Manikandan, S. E.G.S. Pillay Engineering College Department of Department of Computer Science and Engineering Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India University College of Engineering Department of Computer Science and Engineering Tamil Nadu Thirukkuvalai India E.G.S. Pillay Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

The system is a face-based attendance system for students that make use of multi-CNN (Convolutional Neural Network) technology with GPS tracking. The device simplifies the process of taking attendance for both teachers and students while offering precise and effective attendance monitoring. The method makes use of GPS tracking to pinpoint the location of student and get confirmation of their attendance. The multi CNN technology is utilized to improve the attendance's accuracy. Tracking multiple CNN models are combined in the multi-CNN system to increase the process's accuracy for tracking attendance. Additionally, the system makes use of facial recognition technologies to make sure. The appropriate pupil is noted as present. When a student leaves a class, the department head and parents are both notified at the same time. A web-based dashboard and a mobile app for students and teachers make up the system. Every database is kept in cloud storage. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： android application cloud storage Face-based attendance gps technology image recognition

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Advanced transformer model with fine-grained correlation fusion for multimodal Emotion analysis 2

Advanced transformer model with fine-grained correlation fus...

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2nd IEEE international conference on Emerging Research in Computational science, ICERCS 2024

作者： Krishnan, Sundara Rajulu Navaneetha Sangeetha, M. Srinivasulu, Senduru Surendran, R. Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India

ISBN: (纸本)9798331534950

In recent days, Emotion recognition has garnered a lot of attention because of its important applications in human-computer interaction. Human emotion is expressed through a variety of verbal and nonverbal languages, including text, audio, and visual. Current multimodal emotion analysis techniques typically perform poorly because they don't fully utilize the link between various modalities. An advanced transformer model with fine-grained correlation fusion (AT-FGCF) is presented in this research to identify emotion in text, audio, and visual modalities. To enhance the representation of the input audio and video, dense hierarchical feature maps are captured using the Dense Swin Transformer (DSwinT) model. Also, an Emotion- Aware Cognitive BERT (EAC-BERT) is introduced to enhance text representation. Lastly, a fine-grained correlation fusion is carried out to take advantage of correlations between fine-grained features from multimodal data. Our experimental findings show that the suggested method is successful in reaching radical outcomes across two benchmark datasets. That is the datasets IEMOCAP, and MELD all have average accuracy improvements of about 88.35%, and 84.80% respectively. © 2024 IEEE.

关键词： Emotion Recognition

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DEVELOPING A USABILITY engineering ONLINE BLUEPRINT COURSE FOR computer science AND OTHER RELATED MAJORS

DEVELOPING A USABILITY ENGINEERING ONLINE BLUEPRINT COURSE F...

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international conference on Computational science and Computational Intelligence (CSCI)

作者： Mahmoud, Mohammed Univ Jamestown Dept Comp Sci Jamestown ND 58405 USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350361513;9798350372304

Usability engineering is a subject that is related to Human-computer Interaction, focusing on developing elegant, flexible, user-friendly and efficient User Interfaces. In this paper, I am working on developing an online blueprint course for Usability engineering that could be taken by undergraduate and graduate students. This course would he a good fit for computer science, computer engineering, Software engineering, Information Technology, Information Systems and Graphic Design majors. All the aspects of blueprint courses will be explained in great detail, starting off with a definition of a blueprint course all the way through course evaluation and review. The developed course description and objectives will be explained, as well as the selected topics for this course. The course grading criteria and scale will also be demonstrated in the paper. The seven deliverables for the developed Usability engineering course will be thoroughly covered in the paper.

关键词： Usability engineering (UE) Human-computer Interaction (HG) User Interface (UI) User Experience (UX) undergraduate students graduate students computer science (CS) computer engineering Software engineering Information Technology (IT) Information Systems Graphic Design blueprint course

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Contour Detection Algorithm forαPhase Structure of TB6 Titanium Alloy fused with multi-Scale Fretting Features

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Chinese Journal of Mechanical engineering 2024年第5期37卷 499-509页

作者： Fei He Yan Dou Xiaoying Zhang Lele Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Hebei Provincial Key Laboratory of Software Engineering Yan shan University Qinhuangdao 066004China School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijing 100044China

Aiming at the problems of inaccuracy in detecting theαphase contour of TB6 titanium *** combining computer vision technology with human vision mechanisms,the spatial characteristics of theαphase can be simulated to obtain the contour ***,an algorithm forαphase contour detection of TB6 titanium alloy fused with multi-scale fretting features is ***,through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting,the information maps of theαphase contour of the TB6 titanium alloy at different scales are obtained;then the information map of the smallest scale contour is used as a benchmark,the neighborhood is constructed to judge the deviation of other scale contour information,and the corresponding weight value is calculated;finally,Gaussian function is used to weight and fuse the deviation information,and the contour detection result of TB6 titanium alloyαphase is *** the Visual Studio 2013 environment,484 metallographic images with different temperatures,strain rates,and magnifications were *** results show that the performance evaluation F value of the proposed algorithm is 0.915,which can effectively improve the accuracy ofαphase contour detection of TB6 titanium alloy.

关键词： TB6 titanium alloyαphase multi-scale fretting features Contour detection

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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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international Journal of Information Technology (Singapore) 2025年 1-20页

作者： Medikonda, Venkata Reddy Reddy, Tatireddy Subba Yallamandaiah, S. Karthik, Sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an Improved ResNet50 model, while optimal feature selection is achieved through the Improved Artificial Rabbits Optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a multi-Scale Residual Depthwise Separable Convolutional Network (MRDSCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification Improved artificial rabbits optimization algorithm multi-scale residual depthwise separable convolutional neural network

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15th international conference on Interfaces and Human computer Interaction, IHCI 2021 and 14th international conference on Game and Entertainment Technologies, GET 2021 - Held at the 15th multi-conference on computer science and Information Systems, MCCSIS 2021

15th International Conference on Interfaces and Human Comput...

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15th international conference on Interfaces and Human computer Interaction, IHCI 2021 and 14th international conference on Game and Entertainment Technologies, GET 2021 - Held at the 15th multi-conference on computer science and Information Systems, MCCSIS 2021

ISBN: (纸本)9789898704313

The proceedings contain 35 papers. The topics discussed include: a novel augmented reality system to support volumetric visualization in industrial process tomography;categories of user impairment;a service-dominant logic based framework for teaching innovation in HCI;web-based remote ship handling support system using mobile data communication;MAGVI: towards saliency-driven video magnification application for the people with low vision;research on the interface usability of hospital WeChat public platform in the form of internet;ACOUSMA: ubiquitous & intelligent auditory displays;proposal for surrounding information presentation system for the visually impaired;and effect of website color saturation on trustworthiness and visual appeal impressions.

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Understanding the Ethical Implications of Generative AI: A multi-Disciplinary Perspective 5

Understanding the Ethical Implications of Generative AI: A M...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Singh, Gulbir Srivastava, Vivek Kumar, Suneet Bhatnagar, Vivek Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakahnd Nainital India Mmict&bm Haryana Ambala India Mmec Department of Computer Science and Engineering Ambala Haryana Mullana India Ambala Haryana Mullana India Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350379945

The capacity of generative Artificial Intelligence (AI) to develop material such as writing, graphics, and music is one of its defining characteristics. Generative AI has applications in a broad variety of fields, including but not limited to the following: art, literature, software development, education, product design, healthcare, finance, gaming, entertainment, and many more. From the viewpoint of many disciplines, this research study presents the implications of generative AI, considering the technical, educational, legal, and social aspects of the area. By combining concepts from various domains, this article aims to present a comprehensive understanding of the ethical concerns posed by generative AI. Additionally, the research will provide viable frameworks for resolving these issues. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE international conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SMPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SMPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SMPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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