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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sudha, V. Kalaiselvi, R. Jithenthiriya, C.K. Government Polytechnic College Dept. of Computer Engineering Coimbatore India Kumaraguru College of Technology Information Science and Engineering Coimbatore India Kumaraguru College of Technology Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665462167

Participation plays a key role in the classroom culture. When calling out the students' names at the beginning and end of a lecture, lecturers may miss a student, or someone may respond several times to substitute their peers. Face identification can be difficult when using low-definition video and other information systems with the assistance of facial recognition technologies. This necessitates the need to develop a novel device to quickly and accurately identify human faces from images and videos. Many algorithms and techniques have been recently developed to improve the facial recognition accuracy. Deep learning has been recently implemented in various real-time applications to identify and perceive multiple faces at the same time. When it comes to computers, however, it is unrealistic to attempt any of the human identity. The identification of the human identity is a critical aspect of biometrics. The biometric results are consistent with human characteristics. Remote and applied facial expressions can be used to enhance the results. computers that identify and recognize faces can be used for a variety of purposes, including criminal recognition, network surveillance, biometric authentication, and so on. In this paper, an automated system for attendance recognition is proposed. © 2023 IEEE.

关键词： Face recognition

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Deployment Strategies for Wireless Sensor Networks Using Math Modeling Techniques

Deployment Strategies for Wireless Sensor Networks Using Mat...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahal, Nisha Murugan, R. Raizada, Aasheesh College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Wi-Fi sensor networks comprise small, low-value, low-energy sensors configured to interface with the physical surroundings. Due to their constrained power, wireless sensor networks should be deployed and operated in a distributed way. It affords an undertaking for many obligations, which include topology manipulation, energy-green routing, and coverage tracking. This paper proposes numerous deployment strategies for wireless sensor networks using mathematical modeling strategies. Mainly, the proposed techniques use convex optimization and integer programming to research the trade-offs between network insurance, power intake, interference, and topology. Simulation effects display the effectiveness of the proposed techniques in enhancing network overall performance. The paper also discusses extending those strategies to metaheuristics further to improve the scalability and applicability of the proposed method. © 2024 IEEE.

关键词： constrained networks obligations optimization surroundings

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A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS information MANAGEMENT IN RESTAURANT KITCHENS 51

A KUSHO WORDS RECOGNITION METHOD FOR CONTACTLESS INFORMATION...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Usuki, Risa Usuki, Jun Department of Computer Science and Engineering Waseda University Tokyo Japan Department of Information Network and Communication Kanagawa Institute of Technology Kanagawa Japan

Advances in IT technology have led to research into KUSHO recognition, which enables the non-contact operation of devices without the use of devices such as keyboards or controllers. This is a technology that transmits information about the characters written in space towards the camera (hereafter this action is referred to as KUSHO) to the devices. KUSHO has advantages, such as the ability to transfer information without training. In restaurant kitchens, workers transmit various types of information using devices with input functions, however, hygiene considerations are essential when operating equipment in kitchens. In addition, in noisy kitchens, it is also difficult to use voice input, and there are limits to paper-based information transmission. Under these circumstances, in this research, the introduction of KUSHO input technology for non-contact information transfer in restaurant kitchens is being investigated. Therefore, the content handled by kitchen workers for information management is first analyzed and shown to be representable in three input formats. Subsequently, an additional hand signal to be used while doing KUSHO and an improved algorithm for determining them are proposed. Finally, an algorithm for KUSHO recognition of character strings using standard input time is proposed. Experiments on the implementation of these proposals have shown the effectiveness of the new decision algorithm for the addition of a hand signal, with a 100% correct rate. For strings of two or more uppercase alphabetic characters, the recognition rate was 92.5% for all eight types of "KUSHO operation" and the recognition rate was 70.0% for ten randomly selected from 100 different "product names." For "quantity," it was difficult to recognize multiple digits, however 100% recognition rate was achieved by dividing the digits into each single digit 0~9 using an improved hand signal algorithm. Thus, algorithm some problems remain, the effectiveness of all the proposed methods ha

关键词： information management

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Improve the recommendation using hybrid tendency and user trust

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International Journal of information technology (Singapore) 2023年第6期15卷 3147-3156页

作者： Kumar, Jitendra Yannam, V. Ramanjaneyulu Prajapati, Harshit Sahoo, Bibhudatta Babu, Korra Sathya Computer Science and Engineering National Institute of Technology Rourkela Sector 1 Odisha Rourkela 769008 India Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Jagannathagattu Andhra Pradesh Kurnool 518007 India

Recommender systems (RS) are information filtering systems that attempt to predict a user’s rating or preference for an item. It addresses the issue of information overload by providing users with personalized recommendations. RS are typically used for individual recommendations, such as movies, music, and tourists. The tendency-based recommendation technique is easier to use, more convenient, and more precise when compared to the currently available traditional collaborative filtering (CF) techniques. It predicts the final ratings of unrated items using item and user tendency. The personalized tendency approach (PTA) makes recommendations more user-centric using different correlation techniques. We propose two different hybrid approaches to predict the ratings of unrated items. Hybrid approaches are the combinations of Entropy and Clarity-based approach. The recommendations were not user-centric, using only the hybrid approach. This paper merges PTA with the hybrid approach using NHSM similarity. Further, we find a trustee user for an active user. Moreover, we propose a trust-based similarity prediction method. Experimental results show that the proposed approach outperforms the existing methods. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Collaborative filtering (CF) Personalized tendency approach (PTA) Recommender system (RS) Trust

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Testing IoT Protocol Requirements Using Fuzzing and Symbolic Execution: Application to CoAP

Testing IoT Protocol Requirements Using Fuzzing and Symbolic...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Asadian, Hooman Fiterǎu-Broştean, Paul Jonsson, Bengt Sagonas, Konstantinos Uppsala University Department of Information Technology Uppsala Sweden National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798331507428

The rapid expansion of the Internet of Things (IoT) has introduced the need for thorough testing of its protocol implementations to ensure conformance to their specifications and increase their security. This paper investigates the application of two of the major testing techniques, fuzz testing and symbolic execution, to test the implementations of the Constrained Application Protocol (CoAP), a key protocol in the IoT ecosystem. We explore the efficacy of these techniques in discovering requirement violations in CoAP implementations. Focusing on two widely-used CoAP implementations, libcoap and FreeCoAP, we systematically apply both fuzzing and symbolic execution to test conformance with key requirements derived from CoAP's specifications. Our findings demonstrate the strengths and limitations of both approaches, and highlight nine nonconformances in these implementations, most of which have been fixed. Finally, we provide insights into how fuzzing and symbolic execution can be effectively utilized for protocol testing. © 2024 IEEE.

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Bibliometric Analysis of research trends on Graph Neural Networks 20

Bibliometric Analysis of research trends on Graph Neural Net...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Alavi, Meysam Valiollahi, Arefeh Pakravan, Mansooreh Tarbiat Modares University Information Technology Dept Tehran Iran Tarbiat Modares University Electrical and Computer Engineering Dep Tehran Iran

ISBN: (纸本)9798350383942

Graph Neural Networks (GNNs) are a powerful tool for analyzing complex systems and irregular data structures like graphs, revolutionizing tasks like node classification and link prediction. Therefore, regularly assessing scientific outputs to understand the current landscape is crucial. In this regard, scientometric analysis is utilized to assess, interpret, and anticipate the scientific standing of researchers, research institutions, academic journals, and countries on a global scale. Utilizing bibliometric techniques like distribution analysis, co-word networks, and scientific cooperations has proven valuable in unveiling the production trends of researchers in a scientific field, revealing both explicit and concealed dimensions. Despite existing surveys, a significant gap exists in the literature regarding bibliometric studies on the Web of Science (WoS) database in the emerging field of GNNs. This paper uses bibliometric analysis on WoS to explore the evolving GNN research area, covering contributors, collaborations, top journals, and trends identified through keywords and high citations. The research reveals that China, the United States, and Australia lead in publications on neural graph networks. Additionally, the findings underscore a predominant focus on graph neural networks within the realm of computer science. © 2024 IEEE.

关键词： Graph neural networks

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Apple Leaf Disease Detection: Machine Learning & Deep Learning Techniques

Apple Leaf Disease Detection: Machine Learning & Deep Learni...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Sebastian, Merin Suchithra, M.S. Antony, Chippy Maria Department of Computer Science Kerala Irinjalakuda India Indian Institute of Information Technology Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350394580

Early leaf disease detection is essential to mitigate potential crop loss and reduce reliance on chemical treatments. Here we have tried out several Machine Learning and Deep Learning Techniques for automatically detecting apple leaf diseases. Machine Learning techniques like Support Vector Machine, Random Forest, Naive Bayes, K-Nearest Neighbor, Decision Tree, and some Deep Learning methods like Sequential, and VGG-16. We have compared all these models and found that VGG-16 has more accurate results than the other models we used. The recommended model proposes profound figuring out how to improve the arrangement of apple leaf illness. Additionally, it demonstrates the highest validation accuracy of 97.23 percent on the dataset for apple leaf disease. Contrasted with some present futuristic, this approach performs better. Therefore, farmers can use the suggested system to streamline the process of classifying apple leaf disease and aid in the disease's early diagnosis and treatment. © 2023 IEEE.

关键词： apple leaf disease deep learning machine learning VGG-16

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Leveraging Text Mining Techniques for Automated information Analysis 3

Leveraging Text Mining Techniques for Automated Information ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gautam, Sandeep Bhushan, Bharat Yadav, Rakesh Kumar Meena, Y.R. Latha, B. Mehta, Pradnya S. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

These days, the amount of virtual facts to be had has accelerated dramatically, making it difficult for people to manually examine and interpret this sizable quantity of information. To address this project, text mining techniques have emerged as a treasured solution for automatic records evaluation. Textual content mining is the procedure of extracting treasured insights and understanding from textual content-based totally records assets. It involves numerous strategies together with herbal language processing, machine learning, and statistical evaluation to pick out styles, relationships, and tendencies inside unstructured textual content *** of the key advantages of textual content mining is its potential to manner and analyze massive volumes of textual content records in a brief time frame. This makes it especially beneficial for companies that deal with a high extent of unstructured text records, inclusive of social media posts, client critiques, and emails. By using automating the evaluation of this records, textual content mining lets in for a greater green and accurate understanding of consumer preferences, sentiment, and conduct. Further to its performance, text mining additionally affords a complete and unbiased evaluation of textual content facts. Not like manual evaluation, that's subjective and liable to human blunders, textual content mining is predicated on algorithms to extract insights, decreasing the danger of bias and mistakes. That is especially crucial for groups that need to make data-pushed choices primarily based on correct and independent data. © 2023 IEEE.

关键词： Additionally Decreasing Performance Textual

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EEG-Based Seizure Prediction Using CNN and BiLSTM Algorithm of Deep Learning

EEG-Based Seizure Prediction Using CNN and BiLSTM Algorithm ...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Leelavathi, V. Sambath, M. Hindustan Insistitute Of Technology And Sciences Department Of Computer Secience And Engineering Tamil Nadu Chennai India Hindustan Insistitute Of Technology And Sciences Department Of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Automated seizure identification is critical for evaluating epilepsy because manually reviewing electroencephalogram (EEG) data is time- consuming and error-prone. Recent advances in deep learning allowed the creation of automatic seizure identification systems that may evaluate EEG raw time series recordings without requiring the extraction of features manually. The data is divided into EEG epochs and labelled into five categories, including activity related to seizures. Here concentrate on two tasks evaluating seizure activity versus normal activity and categorizing seizure action against every other category. Here two DL algorithms are (CNN) convolutional neural network and a (Bi-LSTM) Bi-directional long short-term memory. These algorithms are taught to divide input EEG data into groups that have been identified. The CNN model is intended to extract significant characteristics from EEG time series data using layers of convolution, followed by pooling layers. However, CNNs might fail to recognize long-term dependencies found in continuous information such as electroencephalogram recordings. On the other hand, the BILSTM model may capture temporal dependencies in sequential data by exploiting both the forward and backward flow of information by using LSTM neurons. This design works well with data from time series, such as EEG recordings, where identifying temporal patterns is critical for accurate categorization. After training and testing the models on the dataset, the prediction results show that the BILSTM algorithm performs much better than the CNN model i both tasks. The BILSTM Model achieves an accuracy of above 98.15%, indicating its effectiveness in accurately classifying seizure activity from EEG recordings compared to the CNN model. © 2024 IEEE.

关键词： Long short-term memory

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Distributed and Cooperative Security Mechanisms for Networked Applications 5th

Distributed and Cooperative Security Mechanisms for Networke...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Singh, Vaishali Bhambu, Pawan Wadhwa, Bhawna Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Cutting-edge applications rely heavily on conversation via PC networks to provide improved functionality, multiplied interactivity, and more to get admission to sources. Cozy verbal exchange and gain control access should be enforced to shield information and different assets. Distributed and cooperative protection mechanisms enable exceptional components of a networked utility to collaborate for you to get the right of entry to resources responsibly, proportion records securely, and authenticate customers. Such mechanisms are regularly designed to offer automated authorization and authentication, secure conversation channels, and a cozy records garage. In addition, they usually include additives, which have users, roles, and permissions to assist in controlling access. Utility developers and IT directors can ensure that networked packages remain cozy no matter the ever-converting safety landscape by adopting allotted and cooperative safety mechanisms. Distributed and Cooperative security Mechanisms (DCSM) are community security mechanisms designed to provide a layered method to defend in opposition to cyber-attacks on networked applications. The use of DCSM permits a shift from traditional networks to an agile and resilient system structure that can react speedily to a capacity chance, mitigating the damaging effect of malicious attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer viruses

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