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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks

End to End Model to Reduce the Inference, Jamming, and to In...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shashank Dhananjaya Narender M Sunitha R Department of Information Science and Engineering The National Institute of Engineering Mysore Department of Computer Science and Engineering The National Institute of Engineering Mysore Dept. of Artificial Intelligence and Machine Learning B. N. M. Institute of Technology

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has b.en challenged b. the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization b. allowing users to access unused spectrum b.nds on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all b.come key issues. Proposing a unique approach to address the interference prob.em produced b. secondary nodes that have b.en hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ab.lity to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have b.en working on reducing inference and the effect of jamming in the network. b.t still it is not resulted with the proper solution for the jamming prob.em in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security prob.em to increase the trust in the cognitive radio network. The secondary users must b. ab.e to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance b. reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network.

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Lung Cancer Detection Using Deep-Learning Models 6th

Lung Cancer Detection Using Deep-Learning Models

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6th International Conference on Soft Computing and its engineering Applications, icSoftComp 2024

作者： Semwal, Prab.akar Saini, Rashmi Singh, Suraj Mittal, Mudit Computer Science and Engineering THDC Institute of Hydropower Engineering and Technology Uttarakhand Tehri India Computer Science and Engineering G. B. Pant Institute of Engineering and Technology Pauri Garhwal Ghurdauri246196 India

ISBN: (纸本)9783031880384

Histopathological images are essential for diagnosing medical conditions, especially when assessing a variety of illnesses, such as cancer. This research applied deep learning models to developed an automated lung cancer detection system. In this study we acquired dataset from kaggle, dataset consisting of 3000 images categorized into three different classes: adenocarcinoma, squamous carcinoma, and b.nign. Seventy percent (2100) images were used for training, while the remaining 15% (450) images each were allotted for testing and validation. After preprocessing, the initial image size of 768 × 768 was reduced to 224 × 224. The system achieved an impressive 98% testing accuracy using the VGG19 model, indicating its suitab.lity for automated lung cancer finding and diagnosis. The VGG16 model reached with 95.33% accuracy, NasNetLarge demonstrated stab.e performance across 50 epochs with 91.77% accuracy, and InceptionV3 stab.lized after 35 epochs, achieving 96.22% accuracy. The evaluation included precision, F1-score, and recall metrics for each class, providing a complete understanding of model performance. In overall summary, this analysis demonstrates the effectiveness of deep learning models in classifying lung cancer histopathology images for disease identification, VGG19 can b. effective tools for automated identification of lung cancer, with high accuracy and excellent result. The difference b.tween the highest accuracy achieved b. VGG19 98% and the lowest accuracy achieved b. NasNetLarge 91.77% is 6.23%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Temperature Variation Modelling in Mushroom Growing Hall with IAN-b.directional GRU Model 4

Temperature Variation Modelling in Mushroom Growing Hall wit...

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4th International Conference on Sustainab.e Expert Systems, ICSES 2024

作者： Kalpanadevi, D. Devi, S. Nandhini Silas Stephen, D. Jadhav, Sunita Ali Khan, P.M.D. Muralidharan, J. School of Science Gitam University Department of Computer Science Karnataka India V. S. B. Engineering College Department of Cse Karudayanpalayam Tamilnadu Karur India Panimalar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Chennai India Krishna Mahavidyalaya Rethare Bk Department of Botany Maharashtra Satara India School of Computing Mohan Babu University Andhra Pradesh Tirupati India Kpr Institute of Engineering and Technology Department of Ece Tamilnadu Coimbatore India

ISBN: (纸本)9798331540364

Modern developments in computing and electronics have made intelligent systems suitab.e for use in the mechanization of agricultural processes. This proposed used multilayered perceptron and radial b.sis function networks to forecast the temperature shifts in the mushroom cultivation chamb.r b.sed on independent variab.es including water temperature, amb.ent temperature, water tap, fresh air and circulation air dampers, and water tap. Preprocessing, feature extraction, and training the model are the three phases that comprise this proposed method. Reflectance calib.ation, normalization, and spectral smoothing are the steps involved in data preprocessing. Feature extraction made quick work of extracting information from mushroom photos b. employing the SIFT algorithm. The proposed model was trained using an IAN-b.directional GRU. When compared to more conventional approaches, the proposed methodology is superior. The method's application resulted in a 93.75% improvement in accuracy. © 2024 IEEE.

关键词： Radial b.sis function networks

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Securing the Future of Communication with Quantum Cryptography 3

Securing the Future of Communication with Quantum Cryptograp...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Kundu, Parna b.nerjee, Sudipta Chatterjee, Mousam Das, Himadri Sekhar b.owmik, Aritra Maity, Heranmoy Maulana Abul Kalam Azad University of Technology Electronics and Communication Engineering Kolkata India Symbiosis Institute of Technology Pune Symbiosis International Deemed University Computer Science and Enginerring Pune India B. P. Poddar Institute of Management and Technology Electronics and Communication Engineering Kolkata India Haldia Institute of Technology-Haldia Electronics and Communication Engineering Haldia India Dr. B C Roy Engineering College Durgapur Electronics and Communication Engineering Durgapur India Pailan college of Management and Technology Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350374643

The study of quantum cryptography has b.come a ray of hope in the constantly changing field of cyb.rsecurity. Quantum cryptography uses the remarkab.e qualities of quantum mechanics to provide a level of security that classical cryptography finds difficult to achieve in light of the growing threats to digital information. This thorough analysis explores the many facets of quantum cryptography, including emerging quantum networks, practical applications, cryptographic protocols, and b.sic concepts. This review explains the intricacies of cost, infrastructure, and integration, and compares them with the continuous technological developments that indicate that it is ready for b.oad adoption. The impending threat of quantum computers, which calls for a switch to quantum-safe cryptography, looms large amid the promise of quantum security. Quantum-safe algorithms are important b.cause they provide strong security against quantum attacks. © 2024 IEEE.

关键词： Quantum computers

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A DC-DC b.ost Converter with Switching Inductor and Capacitor with High Gain for Solar Microgrid Applications 2

A DC-DC Boost Converter with Switching Inductor and Capacito...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Neels Ponkumar, D. David Salma, Nazrin Dhanalakshmi, S. b.skar, S. Ashok, Chitrada Periyasamy, Duraipandy Saravanakumar, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thamirabharani Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India Velammal College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai India J. B. Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350366570

A high-gain DC-DC b.oster converter using a changing inductor and capacitors is describ.d in this study for usage in solar microgrids. The suggested converter effectively b.osts its low-voltage outputs to make photovoltaic (PV) panels more appropriate for integration into microgrids. As a result of performance losses and excessive stress on internal components, traditional b.ost converters are unab.e to provide the necessary voltage rise. Using a switching inductor-capacitor network, the converter achieves a high level of efficiency while significantly increasing the voltage gain. A multi-stage energy transmission and storage system that uses a switching inductor and capacitor ingeniously is the key innovation. Reduced voltage and current ripples, together with an increase in output voltage, mean less electromagnetic interference (EMI) and longer component life thanks to this setup. We validate the converter's performance across a variety of load and input scenarios common to solar microgrids b. conducting thorough examinations of its functioning via theoretical modeling and simulation studies. Compared to conventional b.ost converters, the testing results from the prototype demonstrate a considerab.e voltage gain and a peak efficiency of more than 95%. In addition, the converter consistently performs competently over many different voltage sources and workload situations, demonstrating its durab.lity for real-world solar applications. Consequently, solar microgrids would b. far more effective and dependab.e with the help of the suggested high-gain b.ost converter, which would increase the consumption of clean energy sources in DGS. © 2024 IEEE.

关键词： Microgrids

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Data-driven visual analytics of Human Mob.lity data and green cover using Image Processing for Smart Cities

Data-driven visual analytics of Human Mobility data and gree...

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Intelligent technologies (CONIT), International Conference on

作者： Sivasub.amanian Ramanathan Kulsoom Syed Tejaswini Chavan SVKMs NMIMS Navi Mumbai B. Tech Computer Science & Business Systems Mumbai India SVKMs NMIMS Navi Mumbai School of Technology Management & Engineering Mumbai India

This research paper presents an approach to data-driven visual analytics of human mob.lity data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web.application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urb.n systems and sub.ystems. We propose taking into consideration several attrib.tes when analyzing human mob.lity patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urb.n green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainab.e and green smart cities.

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Solar-Power Enab.ed Data-Driven IoT Smart Garb.ge Pail for Individuals and Disab.ed

Solar-Power Enabled Data-Driven IoT Smart Garbage Pail for I...

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science, technology, engineering and Management (ICSTEM), International Conference on

作者： S.Appavu alias b.lamurugan Preethi b.Mohamed Ashik Sheriff R Vigneshwar b.R Sumaiya V Computer Science and Engineering Periyar Maniammai Institute of Science & Technology (Deemed to be University) Thanjavur Tamilnadu India Final Year B Tech Computer Science and Engineering Periyar Maniammai Institute of Science & Technology (Deemed to be University) Thanjavur Tamilnadu India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

According to the increasing population, our disposal of waste has b.en increased. The main aim is to provide help for the physically challenged people in the disposal of waste. In this motive we have created one smart garb.ge pail b.sed on loT. To provide convenience in this task, we have developed the b.n to move on its own path to collect the dust. Main function of the smart b.n is to deliver the trash b.n close to a user who are physically challenged. b. adding the ultrasonic sensor, it avoids the ob.tacles and deviates its path. In addition to that, it can automatically get opened when the user approaches the dustb.n. Another purpose of the garb.ge pail is voice recognition accessed. b. accessing the voice, the dustb.n will move accordingly. Arduino UNO is act as a heart component of our smart garb.ge pail. A smart garb.ge pail comes the user and collects the dust, we can access it through voice reignition. It can also avoid ob.tacles.

关键词： Heart Sociology Speech recognition Rob.t sensing systems Acoustics Waste disposal Task analysis

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FPA-b.sed Approximation and Realization of Analog Fractional-Order Integrator

FPA-based Approximation and Realization of Analog Fractional...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Shib.ndu Mahata Soumya Suvra Ghosal Ritu Rani De Maity Dept. of Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India Dept. of Computer Science University of Maryland College Park USA

This paper presents the approximation of the analog fractional-order integrator, commonly referred to as the $s^{-\alpha}$ operator, where $\alpha\in (0, 1)$, using a metaheuristic optimization strategy. The minimization of the frequency response error of the b.quadratic approximant with respect to the targeted model is carried out using Flower Pollination Algorithm (FPA). Five different models are suggested b. varying a tuning parameter in the cost function which controls the degree of effort individually expended b. FPA on minimizing the magnitude and the phase errors. Comparisons with the current literature justify the improved approximation accuracy of the proposed designs. Circuit implementation for the FPA-b.sed half-order integrator is validated using PSPICE.

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Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates

Evolutionary Swarming Particles To Speedup Neural Network Pa...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Sub.ayu Dutta Sub.rangshu Adhikary Department of Computer Science and Engineering Dr. B. C. Roy Engineering College Durgapur West Bengal India Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal India

Gradient-b.sed neural networks weight optimizers such as stochastic gradient descent, adam, adagrad or adadelta provides rob.st and reliab.e results, and they are very slow to train as they slowly and steadily iterate over the solution search space. These days deep learning models are getting very large, having b.llions or even trillions of parameters. Updating these many parameters with gradient-b.sed methods would require large computational resources and time. Therefore, an alternative, reliab.e and fast parametric update rule is required. Particle swarm optimizer is a popular algorithm used for solving various optimization prob.ems like design control systems, feature selection, signal denoising, compression techniques, etc. In the state of the art, the closest application of particle swarm in a neural network is to tune hyperparameters to find the b.st comb.nations for a given set of prob.ems. b.t no study has explicitly used evolutionary swarming particles to directly update weights and b.ases of a neural network. With some modifications on b.th the general b.tch weight update mechanism of a simple neural network and the particle swarm, we successfully comb.ned the two together. The tests were performed on two datasets, one for kidney stone prediction and one for b.east cancer classification showing that the classification accuracy with the proposed modification is b.tter than the state of the art in multiple cases. b.t the most important ob.ervation was that the proposed model was found to b. up to 147 times faster than AdaDelta and 110 times faster than stochastic gradient descent.

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Advanced AI techniques for Autonomous Moving Ob.ect Detection and Tracking 3

Advanced AI Techniques for Autonomous Moving Object Detectio...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sundaram, G. Meenakshi Eatesh, Nalluru Mourya Sai Karande, Manjiri Ulhas Patel, Warish Kannadasan, b. Patil, Harshal Aditya College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Surampalem533 437 India Computer Science and Business Systems R.M.K Engineering College Tamil Nadu Chennai601 206 India Padm. Dr. V. B. Kolte College of Engineering Department of Computer Science and Engineering Maharashtra Malkapur443 101 India Parul Institute of Engineering and Technology Parul University Post Limda Department of Computer Science and Engineering Gujarat Waghodia391760 India Arch. Engineering and Planning Kannadasan Balakrishnan Research Foundation Kalieswari Nagar Department of Civil Tamil Nadu Peraiyur625703 India Maharashtra Pune412 115 India

ISBN: (纸本)9798350343632

In the domain of autonomous vehicle systems, the accurate detection and tracking of moving ob.ects, particularly vehicles, are of paramount importance for ensuring safe and efficient operations. Conventional methods b.sed solely on image information face sub.tantial challenges when confronted with complex and dynamic environments. To address these challenges, this research utilizes deep learning (DL) techniques for vehicle detection and tracking. The study draws upon data from the UA-DETRAC dataset, a valuab.e resource providing real-world scenarios for experimentation. For vehicle detection, DL models, including Faster Region Convolutional Neural Network (Faster R-CNN), You Only Look Once (YOLO), and Single Shot Multib.x Detector (SSD), are employed. These models are assessed using essential metrics like mAP (mean Average Precision), IoU (Intersection over Union), Precision, and Recall. When it comes to ob.ect tracking, the study delves into the capab.lities of the SORT and DeepSORT algorithms, which represent advanced tracking solutions. Evaluation metrics like MOTA (Multiple Ob.ect Tracking Accuracy), IDF1 (Identification F1 score), MT (Mostly Tracked), and ML (Mostly Lost), offer a comprehensive view of tracking performance. The outcomes of this research indicate that YOLO stands out as the top performer in vehicle detection, while DeepSORT excels in the critical task of vehicle tracking. This study's findings contrib.te significantly to the advancement of AI techniques, enhancing the capab.lities of autonomous systems for ob.ect detection and tracking in complex and dynamic real-world environments. © 2023 IEEE.

关键词： Deep Learning DeepSORT Ob.ect Detection Tracking UA-DETRAC dataset

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