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Multi-Objective Modified Glowworm Swarm Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

Multi-Objective Modified Glowworm Swarm Optimization Algorit...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Babiyola, A. Kumar, Gotte Ranjith Shridhar, V. Hussein, Abbas Hameed Abdul Alzubaidi, Laith H. Meenakshi Sundararajan Engineering College Dept of Ece Chennai India SR University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Ahl Al Bayt University College of Pharmacy Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The combination of machine learning and Cognitive Radio (CR) technology has generated a lot of interest in research, especially in the field of spectrum management. Even with the possible advantages, high spectral efficiency, low latency, high network efficiency, and enhanced energy economy are only a few of the performance qualities that need to be addressed in order to prepare technology for spectrum management. But accomplishing these goals might frequently provide opposite difficulties. In the context of spectrum management, a new Multi-Objective Modified Glowworm Swarm Optimization (MOMGSO) approach is developed to handle multi-objective optimization issues. In order to achieve global optima, the Glowworm Swarm Optimization (GSO) modification seeks to achieve a balance between the exploration and exploitation processes. This tweak modifies the mutation coefficient, leader selection, and discrimination weight. An analysis is conducted between the suggested technique and current methods in terms of energy efficiency, interference, and throughput. The results of the simulation show that the MOMGWO algorithm outperforms existing techniques in terms of the Pareto front quality. © 2023 IEEE.

关键词： Cognitive Radio Multi-Objective Modified Glowworm Swarm Optimization Multi-Objective Optimization Spectrum Management Spectrum Sensing

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Support Vector Machine with Linear and Radial Bias Function to Classify the Soil Erosion and Land Degradation

Support Vector Machine with Linear and Radial Bias Function ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Borra, Subba Reddy Shreyas, A.V. Alsalami, Zaid Chanti, Yerrolla Ramesh, Gajula Malla Reddy Engineering College for Women Autonomous Hyderabad India Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

The analysis of Land Use and Land Cover (LULC) changes needed update for remotely sensed information for assessing its impacts in erosion. This is especially significant evaluation to regions with semi-arid landscapes the soils cause scarce and appropriate management requirements corresponding LULC for resident conditions to obtain the suitable land use. To addresses this issue, in this article developed the Support Vector Machine (SVM) with linear and Radial Bias Function (RBF) which classifies the land degradation and erosion effectively. The developed SVM with linear and RBF method which is able to handle the mask generation, ranking and overfitting problems. Moreover, it minimizes the computational overhead through dividing the samples from various classes. The images are uniformly rescaled by using min-max normalization and the meaningful features are extracted by utilizing Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) techniques. Then, it is effectively classified by using the developed SVM with linear and RBF technique. © 2024 IEEE.

关键词： Support vector machines

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On Stream Face Mask and helmet detection System Using Machine Learning Techniques 2

On Stream Face Mask and helmet detection System Using Machin...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Sharada, K.A. Mohan, M. Prakash, U. Trisheela, S. Hkbk College of Engineering Department of Computer Science & Engineering Bengaluru India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Within the context of the current day, safety and cleanliness are the most important factors to consider in order to guarantee a healthy and risk-free human existence. Research has constantly proven that basic hygiene is vital, and that wearing a mask in public areas is crucial, in light of the terrible epidemic caused by the Coronavirus. Consider mandating the use of helmets as another precautionary safety measure for users of two-wheeled vehicles. A real-time Yolov3 object detector is used to train the model to recognise face masks and helmets in video footages, live feeds, or photos in order to search for public hygiene and safety. This is done in order to ensure that people are not exposed to potentially hazardous conditions. A neural network is used for the prediction of bounding boxes and the possibilities associated with those boxes over the whole picture. The dataset was gathered via the use of the internet. The model has been taught with the use of internet photos for several classes, such as wearing a mask but not a helmet;not wearing a mask or helmet;wearing a mask but not a helmet;wearing a helmet but not a mask;and wearing both a mask and a helmet. The trained dataset is used mostly for the purposes of classifying items and locating them within the aforementioned categories. In the event that neither the helmet nor the mask is located, the SMTP library module that is supplied by Python specifies an SMTP that may deliver mail to any internet device using SMTP or ESMTP as a client session object. The suggested approach was evaluated using a dataset that included photographs of persons adhering to COVID-19 safety regulations as well as photographs of people adhering to road safety recommendations while wearing helmets. Accuracy and precision are both quite good in the outcomes that were achieved. © 2022 IEEE.

关键词： COVID-19

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A Graph Neural Network and Transformer Encoder Technique for Anomaly and Cyber Threat Detection in Smart Grids

A Graph Neural Network and Transformer Encoder Technique for...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Lakshmanan, M. Adnan, Myasar Mundher Reddy, R. Archana Vasukidevi, G. Aarthy, G. Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science and Engineering Warangal India R.M.K. Engineering College Departmen of Cse Chennai India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350360660

Nowadays, Smart grids enhance traditional power grids by integrating information and communication technologies for more efficient power delivery and management. However, this integration introduces security vulnerabilities and their exploitation leads to cyber-attacks causing severe consequences like infrastructural damage, massive blackouts. This paper presents an effective approach to detect anomaly and cyber threat in smart grids based on deep learning. A novel Graph Neural Network and Transformer encoder (GNN-Transformer encoder) feature extraction method is proposed in this paper for anomaly and cyber threat identification. Initially, the input data is normalized using min-max normalization technique to transform the data into a common scale. The GNN is utilized for spatial feature extraction which conducts representation learning based on topology of power system. The transformer encoder with self-attention mechanism determines the importance of different time steps and captures long-range dependencies. The fully connected layers provided accurate prediction results by mapping the high-dimensional feature vectors to the final output. The proposed method is evaluated on PMU dataset and achieved high accuracy 97.90%, precision 0.82, recall 0.98. The GNN-Transformer encoder method outperformed existing anomaly detection models such as Gaussian Mixture Model (GMM), Spatial Pyramid Pooling with Convolutional Neural Network (SPP-CNN). © 2024 IEEE.

关键词： Cyber attacks

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Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain technology 1

Fog Computing based Privacy Preservation via Distributed Acc...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Thatikonda, Ramya Nandihal, Priya Priyanka, M.S. Mathapati, Mahantesh PhD in Information Technology University of the Cumberlands Williamsburg United States Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Dept.of Cse Bengaluru India

ISBN: (纸本)9798350316469

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices. © 2023 IEEE.

关键词： Blockchain Data Privacy Fog computing Homomorphic encryption Internet of Things Malicious Tampering

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An Underwater Object Detection and Tracking using Bounding Box Prediction and Convolutional Neural Network 1

An Underwater Object Detection and Tracking using Bounding B...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Hussein, Layth Kanth, Tatiraju. V. Rajani Vijayashaarathi, S. Ramachandra, A.C. Bhuvaneshwari, P. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Tvr Consulting Services Private Limited Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, an Object detection and tracking stays energetic and inspiring task in present processer imagination with extensive applications in various industries such as investigation, independent robot directions, and vehicle navigation. The increase in availability of computing power and vast public datasets has made it possible to develop and train robust object identification and tracking systems, driving innovation and advancements in these fields of tracking objects. Object detection faces several challenges such as, where objects are partially or fully hidden by other objects, and variation in pose and scale, which affects an object's appearance. Cluttered backgrounds and changing lighting conditions are also make detection difficult. To overcome these issues, the method of Convolutional Neural Network for Underwater (UW) Object Detection and Tracking (CNN-UWODT) is proposed with DenseNet feature extraction and bounding box for tracking the objects. The process involves with collecting input images from UOT-32 dataset converting to frames extracting features with DenseNet;then predicting the bounding boxes with CNN, and classifying objects with Kernel Extreme Learning Machine (KELM) component to identify key metrics from the input data. The proposed CNN-UWODT presents better results than the traditional You Only Look Once, Version 3 (YOLO-V3)in terms of precision (94 %), recall (96 %) and f1 score (95 %) respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Recurrent Convolutional Neural Network for Crack Detection in Concrete Structures 1

Improved Recurrent Convolutional Neural Network for Crack De...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Almusawi, Muntather Muralidhar, H. Ananthalaxmi Rana Veer Samara Sihman Bharattej, R. Abdul Lateef Haroon, P.S. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bangalore India Department of Civil Engineering Raichur India Saint Leo University Department of Computer Science St Leo United States Ballari Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Ballari India

ISBN: (纸本)9798350352931

Detecting cracks in concrete structures involves advanced imaging and deep learning techniques to identify and assess the extent of structural damage. The problem statement in crack detection methods like an identification of cracks, complex background noise, intricate patterns, and environmental conditions are overcome by segmentation and detection process of this approach. The SDNet2018 dataset and the proposed crack detection method, like U-Net for segmentation, distinguish between cracks and non-cracks. Inception ResNetV2 for pixel-wise feature extraction enhances the identification of crack patterns. The final step involves crack-type detection using an Improved Recurrent Convolutional Neural Network (RCNN). The proposed method achieves performance metrics with an accuracy of 99.67 %, precision of 98.78 %, recall of 99.45 %, and an F1-score of 9 9. 2 1 %.Existing methods like Convolutional Neural Network (CNN+U-Net), K-means with Genetic Algorithm (GKA), Multiscale Crack Detection Network (MSCNet), and Transfer Learning (TL) overcome limitations and provide accurate solution for crack detection in surface areas. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Detection and Classification in Power Transmission Lines Using Discrete Wavelet Transform-Based Swish Recurrent Neural Network 3

Fault Detection and Classification in Power Transmission Lin...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Narasimhulu, N. Irfan, Md. Mujahid Sundaram, S meenakshi Habelalmateen, Mohammed I. Gurumoorthy, Sasikumar Eee Department Ananthapuramu Gooty India Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Engineering And Technology Dhanalakshmi Srinivasan University Computer Science And Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350318609

Transmission lines are an essential power grid component that is responsible for managing electricity from a production station to consumers and substations. The power network's transmission lines are primarily met with various faults. However, to maintain normal operation and minimize damage because of transmission line faults, it requires detecting and classifying faults. In this research, the Discrete Wavelet Transform-based Swish Recurrent Neural Network (DWT-SRNN) is proposed to detect and classify faults in power transmission lines using Deep Learning (DL). Initially, the data is acquired from the transmission line workspace by monitoring devices and sensors. Then, the Butterworth filtering is employed to increase the data quality. The DWT is performed to extract the features that transfer the time domain signal to the time-frequency signal during faulty and non-faulty conditions by decomposition. Finally, the SRNN is established to detect and classify faults in power transmission lines effectively. The existing techniques like Graph Convolutional Neural Network (GCNN), Machine Learning (ML), and Long Short-Term Memory (LSTM) are compared with DWT-SRNN. The proposed DWT-SRNN achieves a better accuracy of 99.89% compared to GCNN, ML, and LSTM respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Feature Selection Based Naïve Bayes Algorithm for Twitter Sentiment Analysis 4

Feature Selection Based Naïve Bayes Algorithm for Twitter S...

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4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Aruna, T.M. Asha, K. Divyaraj, G.N. Pareek, Piyush Kumar Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India REVA University Department of Computer Science and Engineering Bengaluru India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

A intriguing areas of study now is Twitter sentiment investigation. It fuses the data mining methods used to create such systems with natural language processing techniques. The majority of currently available methods for analyzing Twitter sentiment do not do well when presented with messages that are both brief and ambiguous, since this is the only kind of information they take into account. Better classification outcomes are guaranteed if the right collection of features is used to identify emotion in online textual material. The computational difficulty of doing optimal feature selection drives the need for the development of creative approaches to enhancing classifier performance. In this work, an effective method for analyzing Twitter user sentiment was presented. A machine learning model was developed by the suggested method to identify good and negative tweets. Characteristics are extracted after pre-processing, and it's possible that include irrelevant features can lower classification accuracy. For this reason, the model implemented the Vortex Search Algorithm (VSA) to choose the best characteristics and discard the rest. The final tweets categorization is done using the Naive Bayes (NB) algorithm. A total of four Twitter datasets are used for the studies, each measuring a unique set of parameters and made accessible to the public. Marketing, detecting political polarization, and product reviews are just a few of the many areas that may benefit from the suggested system's ability to gauge user sentiment based on tweets. © 2022 IEEE.

关键词： Sentiment analysis

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An Optimal Scheduling of Energy Storage Units in Renewable Energy Systems Using Strength-Pareto Evolutionary Algorithm

An Optimal Scheduling of Energy Storage Units in Renewable E...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Kumari, Nalli Vinaya Kumar, Komuravelly Sudheer Alsalami, Zaid Harshitha, P. Ramadan, Ghazi Mohamad Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ahl Al Bayt University College of Mlt Karbala Iraq

ISBN: (纸本)9798350360660

Dual-stage optimization scheduling model by hybrid energy storage for grid-connected renewable energy systems, is proposed in this paper which focuses on both intra-day and day-ahead phases. In day-ahead phase, model improves economic efficiency by considering of price values at its peak value difference. In intra-day phase, its motive is to decrease renewable energy usage and it's related with the day-ahead schedules using accurate, short-term predictions. Strength-pareto evolutionary algorithm 2 (SPEA2) algorithm for optimization is for multiple objective optimizations, achieving optimal and balanced solutions across multiple objectives. This SPEA2 optimization algorithm efficient handles and it enables system to cost-effectively and firmly operate and optimizing scheduling of grid-connected systems by addressing both the aspects of economic and operational. Overall, the model improves economic efficiency, reduces 6.3% the usage of renewable energy, and improves the sustainability power grid through effective integration and utilization of renewable energy sources. © 2024 IEEE.

关键词： Clean energy

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