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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Hafeezuddin, Mohammed Jadhav, Archana Renuka, P. Sukumar, P. Ghamande, Manasi Vyankatesh Rajasekhar, Kusuma Geethanjali College of Engineering and Technology Department of Eee Telangana India Pimpri Chinchwad University School of Engineering & Technology Department of Computer Science Engineering Maharashtra India Velammal Engineering College Department of Electronics and Communication Engineering Surapet Tamilnadu Chennai India Department of Computer Science and Engineering Tamilnadu Erode India Vishwakarma Institute of Technology Desh Maharashtra Pune India Department of Ece Dr. B R Ambedkar Konaseema Odalarevu Andhra Pradesh India

ISBN: (纸本)9798350388916

The rapid emergence of IoT-powered smart cities has generated substantial apprehensions regarding energy efficiency and security. Current technologies frequently fail to adequately address these concerns in a comprehensive manner. This study presents a hybrid framework that combines GAN with ICA to improve energy efficiency and security in smart city infrastructures. The design of the model utilizes GANs to generate synthetic data and ICA to extract independent characteristics, resulting in improved accuracy and security of IoT systems. The accuracy of our model surpassed that of previous technologies, reaching 97.5%. Additionally, our model achieved a precision of 95.0% and an F1 score of 95.6%. As a result, our model has established a new benchmark in smart city research. The results showcase the model's capacity to enhance smart city operations, rendering it a worthwhile addition to the field. © 2024 IEEE.

关键词： Generative adversarial networks

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Secured Dynamic Opportunistic Routing in Ad-hoc Wireless Network 2

Secured Dynamic Opportunistic Routing in Ad-hoc Wireless Net...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Maheswari, S. Mishra, Nilamadhab Shadaksharappa, B. Sivanesan, T.M. Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India School of Computer Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

In ad-hoc wireless network, the transmission efficiency decreases due to the restriction of channel interference, packet loss, and less bandwidth. Opportunistic routing (OR) is a new routing method which increases the network's efficiency. Though, most of the traditional OR solutions do not resolve the forwarder selection and routing overhead. To solve these issues, Opportunistic Routing for Dynamic Routing (ORDR) with security enhances the OR efficiency. This mechanism uses the mobile node authentication procedure to isolate the malicious nodes. The next transmitter node is selected energetically based on the link strength and the energy to the destination. Based on link quality, this approach discovers the best transmitter from the forwarder list. The proposed ORDR system measures link strength, received signal strength indication, and received packet rate. Experimental results illustrate that the ORDR mechanism provides the best efficiency compared to the baseline mechanisms in terms of delay and improves security. © 2023 IEEE.

关键词： Authentication

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Elevating Grape Leaf Disease Identification Through Integrated CNN-Random Forest Approach

Elevating Grape Leaf Disease Identification Through Integrat...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Banerjee, Deepak Kukreja, Vinay Manchanda, Mahesh Thapliyal, Siddhant Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Uttarakhand Dehradun India Department of Computer Science and Engineering Vardhaman College of Engineering India

ISBN: (纸本)9798350305708

The abstract provides a thorough summary of a study that examines the effectiveness of various grapevine-related classes using a classification methodology. The model's capacity to precisely forecast positive cases inside classes is demonstrated by the research's precision scores, which range from 68.25% to 77.94%. Recall scores also range from 69.49% to 79.71%, demonstrating the model's skill in identifying genuine positive cases. The range of the harmonised F1-Score, which measures the harmony of recall and precision, is 69.35% to 78.01%. Model evaluations have been solidly grounded in large instances in each class, which vary from 590 to 750. The model's proficiency across all classes is demonstrated by the weighted average accuracy, recall, and F1-Score, each of which is impressively high at 85.40%, 86.00%, as well as 84.90%, respectively. Notably, the model's thorough understanding is demonstrated by the micro average accuracy, remembrance, and F1-Score, which reaches 73.78%. These findings demonstrate how well the model performs in accurately classifying a range of grapevine-related problems, significantly advancing tactics for managing and assessing grapevine health. The study's conclusions could have an impact on viticulture and farming strategies. © 2023 IEEE.

关键词： Class Forest Health Precision

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Designing Framework for Intrusion Detection in IoT Based on Spotted Hyena-Based ANN 2nd

Designing Framework for Intrusion Detection in IoT Based on ...

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2nd International Conference on Data science, Machine Learning and Applications, ICDSMLA 2020

作者： Bathula, Archana Muhuri, Samya Merugu, Suresh Gupta, Suneet K. Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Computer Science Engineering Bennett University Greater Noida India Head Research and Development CMR College of Engineering & Technology Hyderabad India

ISBN: (纸本)9789811636899

Internet of Things (IoT) is the platform for resource sharing amidst the computing platforms, mainly memory, power, large data, and storage. Securing the data in the IoT has become a prime concern as the hackers can access through the invaluable information from the IoT database. Once the attacks are incurred on the IoT environment, the loss of data is inevitable, and it can have a major effect on the progress of the IoT platform. In this paper, an Intrusion Detection System (IDS) with improved artificial intelligence is proposed. This paper makes use of standard benchmark datasets from diverse sources for performing the experiment. A well-performing machine learning algorithm called Artificial Neural Network (ANN) is developed with improved network architecture. The usage of a renowned meta-heuristic algorithm called Spotted Hyena Optimization (SHO) is used for selecting the optimal hidden neurons for ANN. The main objective of the improved training is to reduce the error difference between the target and the measured outputs to enhance the detection accuracy. Finally, the experimental outcomes and simulations prove the stability and robustness of the proposed model in terms of a variety of performance metrics over other machine learning models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Identification of Breast Cancer using Jelly fish Based Deep Wavelet Autoencoder 9

Identification of Breast Cancer using Jelly fish Based Deep ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Anbumani, K. Balamanigandan, R. Kumar, T. Rajesh Sasi Kumar, A. Mahaveerakannan, R. Sri Sai Ram Engineering College Department of EIE Chennai India Saveetha School of Engineering Computer Science and Engineering India SIMATS Saveetha School of Engineering Computer Science and Engineering Tamil Nadu Chennai602105 India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Mangalore India

ISBN: (纸本)9798350397376

Mammography, an imaging technology used to detect breast cancer, has recently received more interest from scientists. Screening mammography is the best way to detect breast cancer and other abnormalities. Breast tumours often have two distinct characteristics: mass lesions and microcalcification. A gathering of appropriate picture pre-processing, proceduresare essential for accurate cancer detection and classification in mammograms. Image processing is widely employed in the medical field to improve the diagnostic and treatment processes. When it comes to classification and segmentation, deep neural networks (DNN) have excelled so far. With this in mind, the authors of this paper suggest a deep wavelet autoencoder (DWA) method for image compression by fusing the autoencoder's basic feature reduction function with the picture decomposition capabilities of the wavelet transform. The best attributes for identifying breast cancer are chosen in this study by using the Jelly Fish Search Algorithm (JFSA). When used together, they have a dramatic impact on reducing the size of the feature set required to persist through a subsequent classification job using DNN. The suggested DWA-DNN image classifier was evaluated on two public datasets of breast cancer pictures. The DWA-DNN classifier's performance criteria was compared to that of other existing classifiers, and it was found that the suggested technique excels in comparison across a range of measures. © 2023 IEEE.

关键词： Deep neural networks

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AI-Based Detection and Analysis of Phishing Domains: Leveraging Machine Learning for Enhanced Cybersecurity

AI-Based Detection and Analysis of Phishing Domains: Leverag...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Vijayaraghavan, N. Kumar, Akhil Beni Prathiba, B. Sahaya School of Computer Science and Engineering Vellore Institute of Technology - Chennai Center for Cyber Physical Systems Chennai India Kcg College of Technology Department of Mathematics Chennai India Scset Bennett University Greater Noida India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798331510022

In the ever-evolving landscape of cyber security, the prevalence of phishing attacks poses a formidable threat to information security systems worldwide, compromising data integrity and eroding user trust. This paper introduces an innovative solution harnessing artificial intelligence to identify and analyze phishing domains, particularly focusing on newly registered domains from public databases like WHOIS. This approach is crucial as traditional security measures often prove insufficient against the sophistication of phishing tactics. Our proposed AI-driven system employs an analysis strategy. It utilizes machine learning to conduct an in-depth backend code and content analysis. This involves comparing suspect and legitimate sites for structural and syntactical disparities, utilizing deep learning models trained on extensive datasets of known phishing and authentic domains. These methodologies offer a nuanced evaluation, crucial for distinguishing sophisticated phishing attempts from genuine domains. Additionally, the system excels at identifying malicious links within these domains, enhancing its overall protective capabilities. The efficiency of our solution lies in its capacity for real-time, accurate threat detection. We introduce a probabilistic scoring mechanism that assesses the likelihood of a domain being a phishing site, facilitating informed decision-making for cyber security personnel. Furthermore, our system is designed for seamless integration into existing cyber security frameworks, providing versatility and ease of use. © 2024 IEEE.

关键词： Phishing

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An Electromyography Experiment of the Impact of Food Color and Temperature on Psychological Dynamics in Anorexia Nervosa

An Electromyography Experiment of the Impact of Food Color a...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Lou, Jiacheng Xu, Ziyu Yao, Yousheng He, Caihong Zheng, Junpeng Ying, Fangtian School of Industrial Design Hubei University of Technology China Faculty of Humanities and Arts Macau University of Science and Technology China Hexiangning College of Art and Design Zhongkai University of Agriculture and Engineering China Operations Management Center Guangzhou Wanqu Cooperative Institute of Design China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643685069

Our study endeavors to explore the influence of food color and temperature on the psychological well-being of individuals with anorexia nervosa. Employing electromyography stimulation experiments and concurrently monitoring psychological and physiological indicators, we seek to elucidate the relationships between multi-sensory experiences and the mental health of anorexia nervosa patients. The results reveal that variations in food color and temperature significantly impact emotions, appetite, and the autonomic nervous system of individuals with anorexia nervosa, presenting novel perspectives for enhancing their psychological health. © 2024 The Authors.

关键词： Diseases

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing 22

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lu, Shuaibing Jin, Xin Wu, Jie Zhou, Shuyang Wu, Shen Yang, Jackson Yan, Ran Beijing University of Technology College of Computer Science Beijing China Temple University Center for Networked Computing United States Beijing Jiaotong University School of Software Engineering Beijing China

ISBN: (纸本)9798331509712

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost. © 2024 IEEE.

关键词： Markov processes

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Collaborative MAC Protocol Design for Enhanced Network Lifetime in Mobile Ad Hoc Networks 6

Collaborative MAC Protocol Design for Enhanced Network Lifet...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Verma, Sanjeev Kumar Singh, Digvijay Raghav, Yogita Yashveer Hemelatha, S. Kannimuthu, S. Banchhor, Chitrakant O. Invertis University Department of Applied Sciences Bareilly India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India K. R. Mangalam University Department of Computer Science & Engineering Gurugram India Karpagam College of Engineering Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Information Technology Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Cdma has a huge opportunity to boost the transmission within mobile devices by using surrounding stations to description method that was overheard in order to obtain place a priority. An effective Competitive Middle Authorization (Colloidal carrier) strategy is required to handle the complex data transmission dynamics brought on by transmitting and to capitalize on the advantages of such collaboration. In this article, we present the De las technique, a unique inter Distributing Fuel Position CMAC etiquette for Cellular Advertisements (MANETs). Enhancing MANET functionality in terms of networking longevity and clean energy is the design goal of Donde. The electricity generation of the receive amplification and transmission hardware is taken into consideration in this article using an actual energy usage model. The optimal relay is chosen using a dispersed power best relay technique known that considers young and motivated individual and gps data. Additionally, a cutting-edge benefit for the organization vector configuration is offered to adapt to the changing transmitted strength of the supply and relayed stations with the aim of increasing routing. By using a thorough dynamic simulation, we demonstrate how the planned De su significantly increases networks lifespan in a variety of situations, including those with high electrical energy demand. © 2023 IEEE.

关键词： CC CD-MAC CMAC DCF GPS and PDA MAC Protocol Designs MANET Utility-Based Best Relay Selection WMN

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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