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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance SVM Algorithm

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Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

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Chinese Physics B 2023年第3期32卷 153-163页

作者：叶国栋吴惠山黄小玲 Syh-Yuan Tan Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China Guangdong Provincial Engineering and Technology Research Center of Far Sea Fisheries Management and Fishing of South China Sea Zhanjiang 524088China School of Computing Newcastle UniversityNewcastle NE45TGUK

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this *** the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon *** the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the ***,the key stream can be produced depending on the plain image for a higher ***,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic *** the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is ***,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher ***,experiments show that the the proposed algorithm has a better ***,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

关键词： three-dimensional improved logistic chaotic map(3D-ILM) Rivest–Shamir–Adleman(RSA)algorithm image encryption confusion entropy

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Towards Real Time Traffic Analysis with Reduced Image Quality 21

Towards Real Time Traffic Analysis with Reduced Image Qualit...

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21st IEEE India Council International Conference, INDICON 2024

作者： Mohanraj, Rohit Dayaratna, Erunika Oshani National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal India University of peradeniya Department of Statistics and Computer Science Faculty or science Sri Lanka

ISBN: (纸本)9798350391282

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars. © 2024 IEEE.

关键词： Fast R-CNN Gaussian Blur YOLOv5 ZOD Dataset

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Microstrip patch antenna with high gain and dual bands for secure 5G communication

Microstrip patch antenna with high gain and dual bands for s...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Priya, V. Kalai Sugumar, D. Vijayalakshmi, K. Vanitha, V. Singh, Charanjeet Yasminebegum, A. Department of Computer Science Engineering RMK Collge of Engineering and Technology Tamil Nadu Thiruvallur India Tamil Nadu Coimbatore India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu Chennai India Tamil Nadu Chennai India Department of Electronics and Communication Deenbandhu Chhotu Ram University of Science and Technology Haryana Murthal India Department of Electronics and Instrumentation Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9781032669663

A Microstrip Patch Antenna with High Gain Dual Bands for secure 5G communication is presented in this research. The major goal is to utilize a wide bandwidth antenna operating in the 28/38GHzmillimetre-wave band to obtain a high data throughput. An L-shaped, high-gain microstrip patch antenna is the subject of this research. Rogers RTDuroid 5880 has a relative permittivity constant of 2.2, an angular loss of 0.0009, and a 0.508mm thickness. The substrate for the format is Ansys HFSS software serves as the simulation basis. At 27.84GHz and 39.5GHz, respectively, the concept produced a gain of up to 8.33dB and 7.9dB. At the corresponding resonant frequencies below the parameters’ -10dB line, the impedance bandwidth responses of 1.5 and 4.37GHz are attained. It is recommended to use a small antenna with the following dimensions: 3.2 × 4.9 × 0.508;this antenna offers High Gain and Bandwidth at both bands. Some of the crucial design factors Voltage Standing Wave Ratio (VSWR), gain, and return loss are also properly examined. The suggested antenna has a strong operating band performance, which qualifies it for secure 5G communication. © 2024 The Author(s).

关键词： Natural frequencies

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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INTOCS: Neural Network Inspired Oversampling based Secure and Optimized Transaction Framework for Credit Card

INTOCS: Neural Network Inspired Oversampling based Secure an...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Jigyasha Arora Suyash Bhardwaj Department of Computer Science and Engineering Faculty of Engineering and Technology Gurukula Kangri (Deemed to be University) Haridwar India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Online transactions have transformed commerce and international trade but have brought up new difficulties, especially regarding fraud detection and security. The exponential rise in digital transactions has made reliable fraud detection systems using machine learning and artificial intelligence techniques which are leveraged to detect and mitigate fraud thereby increasing the security of transactions. The paper suggested a model in which data preprocessing using standard scalar along with SMOTE for data balancing is proposed. Data is split into 80-20% train test data based on credit card transactions. An artificial neural network is used to determine credit card fraud. Activation function followed by batch normalization blended with Adam Optimizer and cross-entropy is used to process the computation. Furthermore, classification parameters obtained an Accuracy of 99.95%, Precision of 99%, Recall of 99%, and F1Score of 99%. The result obtained by the proposed method outperforms the existing approach that focuses on secured transactions and helps both the client and server.

关键词： International trade Machine learning Artificial neural networks Credit cards Fraud Information and communication technology Security Servers Reliability Standards

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Advancing Safety in Cooperative Vehicle Platooning Through IoT Connectivity and Convolutional Neural Networks 4

Advancing Safety in Cooperative Vehicle Platooning Through I...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Brindha, G. Rajasekaran, Mothiram Kavididevi, Venkatesh Preetha, J. Yuvaraj, S. Sujatha, S. Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Pine Candle Way Saint AugustineFL United States Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Future transportation systems might benefit greatly from cooperative vehicle platooning, in which vehicles move near one another to decrease air resistance and increase fuel efficiency. However, ensuring everyone in these platoons stays safe is their primary priority. Combining the power of convolutional neural networks (CNNs) and the connection offered by the Internet of Things (IoT), this paper proposes a method to improve the security of cooperative vehicle platooning. Firstly, the interoperability provided by the IoT makes it possible for vehicles to transmit important data such as location, speed, and sensor readings in real time. This makes it easier for platoon members to work together and initiates preventative safety actions. The second use of CNNs is in advanced vision tasks, which include lane tracking, object identification, and anomaly detection. CNNs can alert platoon drivers and the autonomous driving system to possible dangers and course deviations by evaluating data from various onboard sensors, including lidar and cameras. A hierarchical safety design that combines conventional safety features is adaptive cruise control and emergency braking systems with connected IoT devices and perception based on CNNs. Simulated outcomes show that the proposed method significantly improves responsiveness to dangerous situations and decreases accident rates within cooperative vehicle platoons to increase safety. Challenges include providing dependable communication, managing the complexity of sensor data, attaining real-time processing, and mitigating safety issues in dynamic conditions. © 2024 IEEE.

关键词： Adaptive cruise control

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Amel Ali Alhussan Marwa M.Eid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)*** proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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