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Efficient Spectrum Sensing in Cognitive Radio Network Using Multi-Objective Improved Salp Swarm Algorithm 2

Efficient Spectrum Sensing in Cognitive Radio Network Using ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sureka, N. Vasanthakumar, G.U. Swathypriyadharsini, P. Alabdeli, Haider Prashanthi, Bhupathi Agni College of Technology Department of Biomedical Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India The Islamic University Najaf Iraq Griet Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350382952

The Cognitive Radio (CR) has been established as an emerging technique which enables underused or unused spectrum for dynamically enhance the spectral effectively. To enhance the CR performance which is extensively significant to reconfigure or adapt the model parameters. The Multi-Objective Improved Salp Swarm Algorithm (MOISSA) is proposed for spectrum sensing in CR networks. The objective functions such as throughput, energy efficiency and interference are formulated to define the spectrum sensing performance in CR networks. In this paper, the parametric alteration of CR is accomplished through SLSSA which exploits the areas near to individual location to attain its global optimum. The SSA is used to avoid local optima and define the suitable optimal solution evaluation in whole optimization process. In traditional SSA, the Self Learning (SL) rule is applied to provides every learner opportunity to enhance its individual experience through search. The performance of MOISSA is estimated by various metrics like delay, Packet Delivery Ratio (PDR), energy consumption and network lifetime are used with 20, 40, 60, 80 and 100 nodes. The MOISSA attains delay of 13.4s, PDR of 59.7%, 8.55J of energy consumption and 27.5 of network lifetime for 100 nodes which is better when compared to existing method such as Improved Whale Optimization Algorithm with Convolutional Neural Network (IWOA-CNN). © 2024 IEEE.

关键词： Cognitive radio

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Citizen Cane - An Object Detection and Image Description System for the Visually Impaired

Citizen Cane - An Object Detection and Image Description Sys...

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Delhi Section Conference (DELCON), IEEE

作者： Akarsh Navhule Akif Keerthan Byndoor Mohammed Nayaz AN Deepthi Shetty Sarojadevi H Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Visually impaired persons can use informed visual perception to generate understanding of the environment around them to bridge the gap between them and world. This can be done by helping those individuals to identify visual features with the help of linguistic expression through aural/vocal or haptic means. Images are one example of visual perception; humans can identify images, describe it with their words (linguistic expressions) through speech (vocal). The proposed system provides the visually impaired persons with a Smart Cane, that provides the same haptic feedback for object detection, and Smart Glasses that capture the surroundings in the form of images that are captioned based on Encoder-Decoder using deep learning algorithms- Resnet101 which recognizes objects in the images and the LSTM (Long Short-Term Memory) which generates the image caption. The proposed model helps describe the image's features in a meaningful sentence with good accuracy and efficiency.

关键词： Vibrations Visualization Accuracy Prototypes Object detection Linguistics Haptic interfaces Long short term memory Visual perception Smart glasses

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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing 2

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

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An Evolution of Hybrid Bagging Technique in Chemoinformatic and Drug Discovery 2

An Evolution of Hybrid Bagging Technique in Chemoinformatic ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Aluvala, Srinivas Kalshetty, Jagadevi N. Husain, Saif O. Kaur, Jagroop Thind, Harpreet Kaur Sr University School Of Computer Science And Engineering Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Punjabi University Department Of Computer Science And Engineering Patiala India Dayananda Sagar College Of Engineering Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

The drug deals with every living organism on the earth, for classification of drugs based on their action is an important element in drug development. This research paper presents a machine learning model that predicts the action of a drug, utilizing a large drug network. The model integrates J-48 algorithm and random forest algorithm, and form the hybrid bagging technique with the neural network model demonstrating. The model uses the struck2vec data feature, which derived from the large network. Then the data features and dataset are trained on the hybrid bagging technique for drug classification according to the drug properties. The study's findings highlight the important of machine learning models in drug classification which offers a valuable tool for researchers and pharmaceutical companies in the drug development process. The output includes the top classes of drugs, the prediction of action and aiding in new drugs discovery with higher accuracy of 95.83% which is superior over other learning models. © 2024 IEEE.

关键词： Classification (of information)

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Velocity Powered Sprint: Gesture Controlled Car Racing Game

Velocity Powered Sprint: Gesture Controlled Car Racing Game

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： B G Mamatha Bai Priyanshu Pramod Singh Sowmya Tuttagunta Vachaspati Goutam Computer Science and Engineering Nitte Meenakshi Institute of Technology Benguluru Karnataka India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

An innovative real-time car racing game controlled by hand gestures is introduced, employing OpenCV and advanced AI techniques. The primary goal is to offer players an immersive gaming experience by allowing them to steer a virtual car along a racing track using intuitive hand movements. The system utilizes OpenCV’s computer vision algorithms, analyzing live video feeds from a webcam to instantly detect and interpret hand gestures. These gestures, including commands like “move forward,” “move backward,” “turn left,” and “turn right,” act as input signals for directing the car within the game. To enhance gesture recognition accuracy, AI methods are integrated, training a model with machine learning algorithms such as Convolutional Neural Networks (CNNs) to precisely categorize various hand gestures. The identified gestures are then translated into control signals for the virtual car, resulting in a seamless and captivating gaming experience. Featuring visually stunning graphics, challenging racecourses, and competitive adversaries, this project exemplifies the synergy of computer vision, AI, and gaming technologies, offering a user-friendly and immersive hand gesture-controlled car racing game. It not only provides entertainment but also opens up avenues for potential applications in gaming, virtual reality, and human-computer interaction.

关键词： Human computer interaction Solid modeling computer vision Tracking Entertainment industry Games Complexity theory Automobiles Convolutional neural networks Artificial intelligence

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A Task Scheduling Approach for Optimizing 5G Edge Network Performance Through Cloud-Edge Collaborative Computing

A Task Scheduling Approach for Optimizing 5G Edge Network Pe...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Shanthi, M.B. Rao, Vaishali K. P. Sudhakar, K.N. Parameshachari, B.D. Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Rv University Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

This document discusses various techniques that are crucial for 5G mobile communication. These techniques include high-frequency transmission, direct communication, device-to-device (D2D) communication and massive multiple input and multiple-output (MIMO) technologies. The document provides an overview of each technology, including its benefits and limitations. In addition to these techniques, the document also covers mobile edge computing (MEC), which involves distributing computer resources over mobile communication networks. The document outlines eight potential uses of MEC. Lastly, the document proposes a cloud-edge collaborative computing job scheduling system. The proposed technique aims to dynamically assign computer resources to edge devices based on network conditions and current workloads while considering network latency, computation capability, and energy use. Performance scores for the Cloud Only plan range from 4.0 to 4.5, which shows that the plan is very effective. Lower scores from 1.8 to 3.0 obtained by the Edge Only approach imply that its performance is constrained. © 2023 IEEE.

关键词： 5G technology Cloud Edge Resources Device-To-Device Communication Direct Communication High-Frequency Transmission Low Latency Massive MIMO Mobile Edge Computing Multiple-Carrier Techniques Task Scheduling

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Eye Support: AI Enabled Support System for Visually Impaired Senior Citizens 1st

Eye Support: AI Enabled Support System for Visually Impaired...

引用

1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Vasudevan, Vani Thanmai, Thota Yadav, Riya Reddy, Pola Udaya Sowjanya Pradhan, Subrina Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9783031509926

Individuals who have impaired eyesight have a difficult time seeing the tablet’s name, which is written in small characters on the back of the tablet. India features in the "top ten" list for the visually impaired. Numerous technologies are growing in India, allowing us to aid the populace in identifying the tablet’s name, purpose, and other information without assistance from others. Increasingly, technology is being employed for a variety of reasons, and individuals are employing smart gadgets and devices. In the suggested method, users may launch the app on their smartphones, upload or snap a photograph of the pharmaceutical strip, and receive the findings in audio format. The proposed system employs Artificial Intelligence (AI), and Natural Language Processing (NLP) in Python for processing and output in voice format. The smartphone application primarily benefits the elderly, notably those with visual impairments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Optical character recognition

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Predicting Water Potability: Leveraging Machine Learning Techniques

Predicting Water Potability: Leveraging Machine Learning Tec...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： N Laya J Shruthi Shetty Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

In recent times, the spotlight has been on understanding and forecasting water quality, owing to the variety of pollutants that pose potential harm. This research aims to advance strategies for managing and minimizing water pollution risks by examining the application of machine learning models for water quality classification. The algorithms under scrutiny include Logistic Regression, K-Nearest Neighbors Classifier, Support Vector Machine, Decision Tree, and Random Forest Classifier. These models underwent testing on a wate_potability dataset with 10 features, and their performance was assessed using various accuracy metrics, precision, fl_score and recall, where parameter tuning is also applied lastly for increasing better performace. The findings indicate that the proposed models effectively categorize water quality, with the Random Forest Classifier emerging as the most accurate in making predictions with 80% before parameter tuning and with 81% after parameter tuning. The. pkl pickle library has been employed to save the random forest classifier, ensuring its accessibility and usefulness in the future. Through this process, the ultimate model has been stored, facilitating its application in forecasting outcomes on unseen datasets in upcoming *** research where the prediction is made on the dataset to categorize the water as potable or non-potable.

关键词： Accuracy Machine learning algorithms Water quality Predictive models Libraries Classification algorithms Decision trees

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Video Summarization on E-Sport 1

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1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Vasudevan, Vani Darshan, M.R. Kumar, J. V. S. S. Pavan Gaonkar, Saiel K. Reddy, Tallaka Ekeswar Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Video summarizing is a useful technique for extracting the most important information from long videos. The "Video Summarization on E-sport" technique, which hasn't been extensively used in the esports industry yet attempts to automatically create highlights from e-sport game footage. This program, which users may input an e-sport game into, can produce summarized movies that capture the most thrilling parts of the game without the need for specialized equipment or experienced video editors. The suggested approach leverages AI, machine learning, and certain Python modules to produce highlights automatically, as opposed to the conventional way of making summarized movies, which is labor and time intensive. Users may save important time and effort by using the technology to automatically summarize key game moments by analyzing audio and text cues. The future of e-sports content creation may be greatly impacted by this technology, which has the potential to revolutionize the way e-sport game footage are summarized. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Blockchain Voting: A Solution to the Challenges of Traditional Electoral Systems

Blockchain Voting: A Solution to the Challenges of Tradition...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： M R Sowmya Manash Hamal Avishek Chaudhary Johnson Kumar Patel Soyuz Man Shrestha Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Voting is a prominent part of the political process, especially in a democratic country like India, where it plays a key role in electing government officials and shaping public policy. Conventional paper-based methods are easy to implement but have considerable limitations, such as security vulnerabilities, paper usage, and the requirement for a large workforce. Common issues such as booth capturing, vote tampering, and manipulation compromise the integrity of elections. To deal with these challenges, blockchain technology offers a solution by providing an immutable and decentralized voting system. This method guarantees that, once added, data cannot be modified, thus preventing manipulation. The decentralized nature of the system allows for continuous availability, as any node failure can be compensated by another node. Moreover, it reduces the need for manual oversight, speeds up the voting process, enhances security and provides transparency, which traditional systems lack. Incorporating blockchain technology into the voting process could significantly enhance the integrity and efficiency of elections, paving the way for a more trustworthy process.

关键词： Voting Manuals Software systems Blockchains Security Public policy Information technology

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