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Design and Development of S-Shaped Antenna for Wireless Networks

Design and Development of S-Shaped Antenna for Wireless Netw...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： Vanitha V P Kishor Kumar Y. Sreenivasulu Ruchira Rawat S. Ramesh Jogendra Kumar Department of Electronics and Communication Engineering AarupadaiVeedu Institute of Technology Vinayaka Missions Research Foundation (Deemed to be University) Chennai Tamil Nadu India Department of Electronics and Communications Engineering Ravindra College of Engineering for Women Kurnool Andhra Pradesh India Department of Electronics and Communications Engineering Sreenidhi Institute of Science and Technology Hyderabad Telangana India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computational Intelligence Institute of Artificial Intelligence and Machine Learning Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

In this study, a linear array of S-shaped Microstrip patch antennas is created and examined. IE3D V15.2 software was used to design, study, and simulate the antenna. The ground plane is 40x70 mm, and the patch is 30x60 mm, we'll say. In only one band, the bandwidth has increased. Gain at 2.43271 GHz is 3.68152 dBi, whereas bandwidth at 1.8744 GHz is 63.05 percent. Substratum thickness of 1.6 mm, dielectric constant of 4.2, and loss tangent of 0.0013 are taken into account in this computation. This antenna is mostly used in wireless systems.

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Multimodal Sentiment Analysis using Kernel Based Support Vector machine

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Ashish Khanna Deepak Gupta Madhumala R B Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Support vector machines Sentiment analysis Analytical models Sensitivity Veins Tagging Probabilistic logic

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Contrastive learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

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IEEE International Conference on Data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers Data models Data mining Recommender systems

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion

RecCoder: Reformulating Sequential Recommendation as Large L...

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IEEE International Conference on Data Mining (ICDM)

作者： Kai-Huang Lai Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master.

关键词： Adaptation models Codes Accuracy Large language models Semantics Cognition Data models Data mining Recommender systems Testing

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AKM-FCCI: Secure Authentication and Key Management Mechanism for Fog Computing-Based IoT-Driven Critical Infrastructure

AKM-FCCI: Secure Authentication and Key Management Mechanism...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Vijay Karnatak Neha Tripathi Amit Kumar Mishra Mohammad Wazid Ashok Kumar Das Mohsen Guizani Sachin Shetty Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Critical infrastructure refers to the key systems, assets, and facilities, whether they are physical or virtual, that are necessary for the overall functioning of a country. As our reliance on technology and networked systems continues to expand, so does the significance of taking precautions to protect critical infrastructure from being compromised by cyberattacks. We need some security schemes to secure the communication happening in the critical infrastructure devices. Therefore, in this paper, we focus on the design of an authentication and key establishment scheme, which is used in the critical infrastructure to secure its data transmissions. A secure authentication and key management mechanism for fog computing-based IoT-driven critical infrastructures (in short, AKM-FCCI) is proposed in the paper. We then provide the network model and threat model of the proposed AKM-FCCI to explain its deployment and organization of devices and servers. The threat model further explains the various threats of this communication environment. In addition, the security analysis of AKM-FCCI is presented to demonstrate that it is secure against the many different kinds of attacks that could be launched against it. The comparisons demonstrate that the performance of AKM-FCCI is superior to that of the other currently used schemes. In addition to that, it offers a high level of security and utility. Therefore, the proposed AKM-FCCI is appropriate for use in protecting critical infrastructure equipment against a wide variety of threats.

关键词： Threat modeling Wireless communication Performance evaluation Authentication Organizations Critical infrastructure Safety

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Real-Time Health Data Analytics in IoT-Connected Wearable Devices

Real-Time Health Data Analytics in IoT-Connected Wearable De...

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Emerging technology (INCET), International Conference for

作者： Jyostna Devi G S Venkatakiran V Nuthan Prasad Swathi Pai M KDV Prasad Sheetalrani Rukmaji Kawale Department of Electronics and Communication Engineering Sri Venkatesa Perumal College of Engineering (AUTONOMOUS) Andhra Pradesh India Department of Electronic and Communication MSRIT Bangalore Karnataka Dept. of Artificial Intelligence and Machine Learning NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Computer Science Karnataka State Akkamahadevi Women University Vijayapura Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

This research paper focuses on the revolutionary effect of instant in-person health analytics by IoT -connected wearable medical devices on health results. By the exploration of currently available literature, technical systems and showing case studies, we provided strong evidence that real time monitoring improved patient outcome and made healthcare delivery better. The research unveils the remarkable sequestration of advancing wearable device technology that can capture and analyze essential health factors in the current state. Through our representation of the remote patient monitoring, chronic disease management, and fitness tracking trends, we show the effects of those advancements being clearly pronounced as early disease detection, personal interventions, and higher patient engagement. On the other hand we tackle the mentioned issues such as data privacy and security presenting new methods as possible options to minimize the dangers. Stakeholders in the healthcare and technology sector obtain useful information by analyzing the results of this study, as they can now explore the power of real-time health data analytics to completely change patient care and design upon healthcare delivery in the future.

关键词： Data analysis Accuracy Medical services Real-time systems User experience Stakeholders Security

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EM-PAD: An Effective Mechanism for Phishing Attack Detection

EM-PAD: An Effective Mechanism for Phishing Attack Detection

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Aakash Saksham Mittal Mohammad Wazid Amit Kumar Mishra Ashok Kumar Das Sachin Shetty Mohsen Guizani Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computational Modeling and Simulation Engineering Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

In the present era, the increasing number of network devices and ubiquitous computing leads to the flow of enormous amounts of data traffic, including sensitive and confidential information, on the internet and carrying out commercial and banking transactions online. This gives cybercriminals an opportunity to launch an attack like phishing to steal confidential information from the user and gain unauthorized access. This can be mitigated by the help of an intelligent machine learning-based phishing detection system, which can detect potential phishing attacks and take appropriate action. In this paper, we have addressed this major cyber issue and proposed a machine learning-based phishing detection scheme (in short, EM-PAD), which is trained on a benchmark dataset and evaluated on standard metrics: F1-score and Accuracy. The proposed model is compared with different existing schemes based on Accuracy, indicating that it has outperformed them with remarkable results.

关键词： Wireless communication Measurement Accuracy Phishing Computational modeling Banking Benchmark testing

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Research on Graphic Design Image Processing technology Based on Newton's method in Photoshop

Research on Graphic Design Image Processing Technology Based...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Mohammed Hussien Al-Fatlawy M. Sahaya Sheela Santosh Kumar Yadav V Srinivasan S. Gopalakrishnan N V Uma Reddy College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Electronics & Communication Engineering IES College Of Technology Bhopal MP India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Kelambakkam Tamil Nadu India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bengaluru

The term "computer graphics and image processing technology" refers to a technology that is based on computers and makes use of software to process graphics and images in a comprehensive manner. Since its introduction, it has been the subject of extensive research and practical use, and as a result, it has steadily evolved into a fully formed and flawless technical system. Image editing, image composition, color correction, the generation of functional color effects, and other related tasks are among the primary features included in Photoshop. The title bar, the menu bar, the picture editing window, the status bar, the toolbox, the control panel, the drawing mode, the file format, the file size, the color setting, shortcut keys, and the color mode are all components of the operational interface. Studying the image processing technology used in graphic design based on NM (Newton's technique) in Photoshop platform is the focus of this research project. Using NM as a foundation, construct a graphic design image processing model. in order to make the global convergence better and reduce the goal function as much as possible. To verify that the goal function is moving in the desired direction, the modified global criterion of NM is applied. According to the findings, the new and improved algorithm has a running time that is 3.67 percent and 2.25% less than that of the NM and the inexact Newton method respectively. The root-mean-square error of the reconstructed image of the cross-section with a diameter of 19mm is controlled to be approximately 5.03%. This error decreases as the amount of Gaussian noise increases. Even if there is significant interference from Gaussian noise when the algorithm reconstructs sections of varying diameters, the quality of the reconstructed image of the improved NM has only a little amount of impact on the reconstructed image.

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Certain Electroenchaphalography Signal Analysis for Artifact Removal Using Multiwavelet Transform

Certain Electroenchaphalography Signal Analysis for Artifact...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： Basavesha D Madhumala R B Prashanth Venkatareddy Sapna Kadakadiyavar Piyush Kumar Pareek Surabhi K R Department of Computer Science & Engineering Shridevi Institute of Engineering & Technology Bengaluru Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore Department of Electrical & Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru Department of Electronics and communication Sambhram Institute of Technology Bangalore Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

This work presents an efficient strategy for eliminating mixed artifacts that are present in electroencephalogram recordings. All three categories of recordings are suitable candidates for use of the approach. The presence of the noise source makes it more challenging to do an analysis of the EEG and get clinical information. This is because the EEG is being measured in a noisy environment. EEG signals are biological signals that take place in the temporal domain and are multidimensional, non-stationary, and irreproducible. EEG signals are also known as electroencephalogram signals. Electroencephalogram signals are sometimes often referred to as EEG signals. This suggests that the statistical qualities of these people do not remain the same over time as shown by the fact that this is the case. It is anticipated that it will offer information on the activities that are taking place at the level of the ensemble of excitatory pyramidal neurons, with a temporal resolution scale of milliseconds. This prediction is based on the fact that the scale measures time in milliseconds. It is highly difficult to derive any useful information from scalp EEG since it includes a substantial amount of background noise and artifacts, and the particular area from which it originates cannot be determined. As a result of these factors, it is quite difficult to analyze scalp EEG. This is because the background noise and artifacts might make it difficult to determine the source of the signal. As a result of this, it is of the utmost importance to develop specific methods for reducing the occurrence of artifacts in EEG recordings, such as the one in question. These methodologies need to be created as quickly as time permits. In this piece of research, we suggest making use of the Multiwavelet transform to clean up the EEG data and get rid of any artifacts that could be present. Following the use of the GHM algorithm, which cleans up the noisy signal produced by the Multiwavelet transform, th

关键词： Time series analysis Scalp Transforms Electroencephalography Time measurement Recording Noise measurement

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Innovations in Voice Navigation Using Object Detection to Empower the Visually Impaired

Innovations in Voice Navigation Using Object Detection to Em...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ranjeet Suryawanshi Annepu Arudra Dankan Gowda V Mathu Uthaman Swathi Pai M VK Satya Prasad Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth's college of Engineering Kolhapur Maharashtra India Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology R T Nagar Cholanagar Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Mahaguru Institute of Technology Kattachira Kayamkulam Alappuzha India Department of Artificial intelligence and machine learning NMAM Institute of Technology Nitte Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

The project focuses on addressing the challenges faced by visually impaired individuals in navigating environments, especially when encountering obstacles or misplacing essential tools like walking sticks. A novel solution is proposed: a voice-controlled smart stick, designed to enhance the autonomy of visually impaired people in their daily activities without reliance on others. This research introduces a sophisticated walking stick equipped with ultrasonic sensors, operated via voice commands for ease of use and accessibility The voice-controlled smart stick is distinguished by its user-friendly interface, rapid responsiveness, minimal power consumption, lightweight design, and ease of folding and handling. This project not only offers a practical solution for the visually impaired but also signifies a significant advancement in assistive technology, enhancing independence and safety in daily life.

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