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International Conference on Image Processing and Capsule Networks, ICIPCN 2020

作者： Alrumaih, Abdulrahman Alsabah, Ruaa Aleqabie, Hiba J. Mjhool, Ahmed Yaseen Al-Sabbagh, Ali Baldwin, James Law College Al-Nahrain University Baghdad Iraq College of Science Computer Science Department University of Kerbala Kerbala Iraq Information Technology Research Development Center University of Kufa Najaf Iraq Computer Engineering Department Al-Taff College University Kerbala Iraq Ministry of Communication ITPC Babylon Iraq Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9783030518585

Social media platforms are witnessing significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Twitter is a widely known social media platform in which users from different cultural and linguistic backgrounds. It is also valuable in studying a large amount of data and turning it into valuable information that can be useful in decision making. This study aims at investigating the sentiment orientation of the textual features as well as the emojis-based features of Arabic comments posted on social media, Twitter, during the World Cup 18 event. It also measures whether different results will be obtained when the text is analyzed in isolation from emojis. This paper presents the approach that involves extracting thousands of tweets, implementing the sentiment analysis, on the texts, and the emojis separately. This analysis has been conducted once via a using an artifact and once again manually, then a comparison between the results is made. This comparison shows that emojis support the sentiment orientation in the text. However, it can be relied solely neither on the text nor on emojis as they complement each other. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Complex networks

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The development of 6-G Technology in Integration with AI type of Synergy

The Development of 6-G Technology in Integration with AI typ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： D. R. Primmia M. Mahabooba J. Karpagam Komal Sharma Akhilesh Singh Sharon Manoj Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College TN Department of Computer science and Engineering (CS and E) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education TN Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Civil Engineering IES College of Technology Bhopal M.P. India Department of Information Technology Hindustan Institute of Technology and Science Kelambakkam Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this scientific study, the mutual connection between 6G technology, which and machine intelligence (AI) comes center stage. Delving on to the potential connections, the paper suggests that the merging of 6G and AI holds the secret to opening new possibilities in diverse fields. A thorough analysis emerges, showing the transformative effect that this mixture can have on the critical sectors such as medical care, transport, augmented reality education, resource management, robots, as well as solving challenges in public safety and battle. While the paper highlights the hopeful possibilities that lie to come it carefully admits the simultaneous increase of risks. In light of this understanding, the paper provides a detailed review of the security risks that are inherent in this technology nexus. Furthermore, a careful study of potential prevention methods is given, underscoring the necessity of proactive measures for maximizing the benefits while protecting against possible dangers. The merger of 6G and AI, as anticipated in this academic discourse, arises not only as a trigger for revolutionary developments but also as a territory needing careful consideration of ethics and security imperatives. The paper consequently acts as a leading light, directing the debate towards a future what the fusion of 6G while AI adds positively to our progress while carefully handling related challenges.

关键词： 6G mobile communication Reviews Prevention and mitigation Merging Medical services Public security Security

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Spatially Non-Stationary XL-MIMO Channel Estimation: A Three-Layer Generalized Approximate Message Passing Method

arXiv

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arXiv 2024年

作者： Tang, Anzheng Wang, Jun-Bo Pan, Yijin Zhang, Wence Chen, Yijian Yu, Hongkang de Lamare, Rodrigo C. The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang212013 China The Wireless Product Research and Development Institute ZTE Corporation Shenzhen518057 China The Centre for Telecommunications Studies Pontifical Catholic University of Rio de Janeiro Rio de Janeiro22451-900 Brazil The School of Physics Engineering and Technology University of York YorkYO10 5DD United Kingdom

In this paper, the channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of near-field (NF) spherical wavefront effects and spatially non-stationary (SnS) properties. Due to the diversity of SnS characteristics across different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase estimation scheme that decouples the problem into multiple subchannel estimation tasks. To solve these sub-tasks, we introduce a novel three-layer Bayesian inference scheme, exploiting the correlations and sparsity of the SnS subchannels in both the spatial and angular domains. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS properties in the spatial domain, and the third layer effectively decouples each subchannel from the observed signal. To enable efficient Bayesian inference, we develop a three-layer generalized approximate message passing (TL-GAMP) algorithm that combines structured variational message passing with belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed TL-GAMP algorithm, demonstrating its robustness across various channel environments, including NF-SnS, NF spatially stationary (NF-SS), and far-field spatially stationary (FF-SS) scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

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development of Healthcare Monitoring System with Pollution Control in Industrial Sectors Using the Internet of Things

Development of Healthcare Monitoring System with Pollution C...

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International Conference on Inventive research in Computing Applications (ICIRCA)

作者： N Raja Rajeswari G Arunkumar M Senthil Vadivu Manish Gupta R Sethuraman M Rajkumar Department of Mechanical Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Vellore Tamil Nadu India

Rapid improvement in industrialization and urbanization leads to various impacts on environmental conditions. This leads to a rise in pollution that causes various constraints on human health. Earlier prediction and maintenance of pollution is the solution to obtain a greener ecosystem. This is done through machine learning techniques adopting the Internet of Things. The Internet of Things helps to obtain a smarter environment through real-time monitoring with the control system. Thus machine learning helps to analyze the amount of toxic substances that are emitted from the industries and recorded periodically. They are represented as online controlling and monitoring systems. The sensors and actuators with control devices are used to record the external environmental conditions. The prior intimation of the healthcare is monitored appropriately and intimated to solve earlier. The classification and detection of infections and diseases are done through image processing techniques. Thus the proposed system helps to obtain automatic detection of healthcare and pollution detection systems through machine learning with computational systems.

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Comparative Study of Spatial Diversity Techniques in Log-Normal Shadowing Channels

Comparative Study of Spatial Diversity Techniques in Log-Nor...

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Device Intelligence, Computing and communication Technologies, (DICCT), International Conference on

作者： Raj Ranjan Deepak Upadhyay Mukesh Soni Rashmi Sharma Mridul Gupta Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This study explores the effectiveness of spatial diversity techniques in log-normal shadowing channels, which are crucial for enhancing the reliability and efficiency of wireless communication systems. Extensive simulation and analysis are done to investigate different spatial diversity techniques i.e. maximal ratio combining (MRC), space-time block codes (STBC) and selection combining (SC) in combination with different signal-to-noise ratio (SNR) and shadowing variance conditions. We evaluate the effectiveness of the techniques through performance metrics including Bit Error Rate (BER), spectral efficiency, and signal-to-noise ratio. The results show that MRC and STBC perform better in most scenarios, which highlight the necessity of antenna diversity for system reliability and spectral efficiency. These findings while signaling the possible future enhancements of the wireless communication systems working under shadowed propagation environments are of paramount value in optimizing the system designs.

关键词： Fading channels Spectral efficiency Spatial diversity System performance Diversity reception Bit error rate Reliability Optimization Shadow mapping Signal to noise ratio

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Teacher-Student Complementary Sample Contrastive Distillation

SSRN

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SSRN 2023年

作者： Bao, Zhiqiang Huang, Zhenhua Gou, Jianping Du, Lan Liu, Kang Chen, Yunwen School of Computer Science South China Normal University South China Normal University Guangdong Guangzhou510631 China School of Artificial Intelligence School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia Research and Development Department DataGrand Inc. Shanghai Shanghai201203 China

Knowledge distillation (KD) is a widely adopted model compression technique for improving theperformance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that addresses these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher’s supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques. © 2023, The Authors. All rights reserved.

关键词： Distillation

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Computational Methods for Analysing Biometric Systems

Computational Methods for Analysing Biometric Systems

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Fahim Multani Sheela Bijlwan Viyat Varun Upadhyay Bhawna Goyal Ayush Dogra Dawa Chyophel Lepcha Department of Computer Science Engineering IES College of Technology Bhopal Madhya Pradesh Uttaranchal School of Computing Sciences Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Mechanical Engineering GLA University Mathura Mathura University Center for Research and Development Chandigarh University Mohali Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department Electronics and Communication Engineering Chandigarh University Mohali Punjab India

The use of biometric data, such as various anatomical and behavioural attributes in automatic identification and authentication systems for access control, surveillance, computer security, and law enforcement are increasing. Several nations are already or will soon begin to use biometric technology. In this paper, there is a in detail analysis of innovative biometric systems and also the research study of how the computational techniques used intelligent data system in order to analyse biometric system, The efficacy of biometric technologies will depend on how well they work. Biometric system evaluation examines how well biometric systems adapt to particular characteristics. Planning and carrying out biometric tests are frequently handled by an impartial third party. A measure of confidence in the validity of the claim might be one of the options the algorithm offers. Applications that validate a user's address or confirm their identity at the point of sale are examples of verification applications.

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A Novel IoT Framework for Smart Agriculture Using Machine Learning Algorithms

A Novel IoT Framework for Smart Agriculture Using Machine Le...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Dankan Gowda V Rama Chaithanya Tanguturi Inumarthi V. Srinivas Tadi Satya Kumari Shravan Chandak N Sudhakar Reddy Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Analytics Woxsen University Hyderabad India Prin. L.N. Welingkar Institute of Management Development and Research Lakhamsi Napoo Rd Opposite Matunga Gymkhana Matunga East Mumbai India Department of Computer Science & Engineering Aditya College of Engineering and Technology Surampalem India Symbiosis Centre for Management Studies Pune Symbiosis International (Deemed University) Pune India Department of ECE School of Engineering Mohan Babu University Tirupati India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This paper aims to explore the pertinence of future study and usage of IOT in dealing with the use of artificial intelligence in farming as one of the effective ways on how a better practices can be promoted in the farming sector. Consequently, the objective of this paper is to develop a new architecture for IoT usage that can be instituted systematically for monitoring and assessing productivity in the agricultural sector with a view of arriving at the right decision. The framework makes use of the best of what is currently available in machine learning for yield forecasting, the resource for example water and energy, among others. This was, in turn, followed by experiments and simulation and where differential features such as crop yield prediction, resource usage, and amongst others, were enhanced. Some of the suggested ideas have been owning to below 5% accuracy for the crop yield forecasts and lowering the usage of water by a quarter, and accurate detection of the temperature and relative humidity for integration of energy. Thus, one can suppose that the offered framework can be helpful in solving the problems connected with the management and sustainment if the resources for agriculture. For such reasons, the offered framework has a potential to redefine the contemporary agriculture since this framework offers actual advice to farmers.

关键词： Smart agriculture Temperature measurement Productivity Accuracy Machine learning Humidity Temperature control Water resources Crop yield Farming

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Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems

Cognitive Twins for Predictive Maintenance and Security in I...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： B. Venkata Swamy Pareshwar Prasad Barmola Senthil Thangavel S. Kaliappan Hardik Patel Girish Abhyankar Department of Chemistry B V Raju Institute of Technology Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Division of Research and Development Lovely Professional University Phagwara India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The increasing complexity and interconnectedness of Internet of Things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. This paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. The framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. The framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%.

关键词： Biological system modeling Decision making Reinforcement learning Software systems Real-time systems Natural language processing Maintenance Internet of Things Security Predictive maintenance

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RETRACTED: Energy efficient multi-tasking for edge computing using federated learning

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International Journal of Pervasive Computing and communications 2022年第3期20卷 18-32页

作者： Mukesh Soni Nihar Ranjan Nayak Ashima Kalra Sheshang Degadwala Nikhil Kumar Singh Shweta Singh Department of CSE University Centre for Research and Development Chandigarh University Mohali India School of Information Science Presidency University Bengaluru India ECE Department Chandigarh Engineering College Landran India Department of Computer Engineering Sigma Institute of Engineering Vadodara India Department of CSE Maulana Azad National Institute of Technology Bhopal India Electronics and Communication Department IES College of Technology Bhopal India

Purpose The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage. Design/methodology/approach The new greedy algorithm is proposed to balance the energy consumption in edge computing. Findings The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent. Originality/value The results are shown in this paper which are better as compared to existing algorithms.

关键词： Edge computing Federated learning Energy balancing Greedy algorithm Multi-tasking

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