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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Manoj Kumar s C.L. Monica sunith babu L v Manikandan Dinesh subramani subhash Malik Department of Mechanical Engineering Madanapalle Institute of Technology & Science Angallu India Department of Environmental Science S&H 1 V.R.Siddhartha Engineering College Vijayawada India Department of Mechanical Engineering M. S. Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering JAIN (Deemed-to-be-University) Bengaluru India Department of Artificial Intelligence and Data Science Nandha Engineering College (Autonomous) Erode India Department of Mechanical Engineering Maharishi Markendeshwar Engineering College Mullana India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Mushroom cultivation leads global agriculture due to its impact on worldwide trade. One of the main challenges in this area is optimal weather, especially temperature and humidity. Unfortunately, traditional farming's inability to achieve these conditions often leads to low yields. The recommended procedure involves collecting sensor data, constructing the model, and training it. Preprocessing includes spectral smoothing, normalization, and reflectance calibration. sensors measure motion, temperature, light, and soil moisture. Photo feature extraction uses edge detection to identify objects. The model is trained using NTM. The suggested NTM-based model beats top-tier models like Random Forest (RF) and support vector Machine (svM) with 92.35 percent accuracy. This increasing accuracy shows the model's mushroom production optimization. This proposed uses sensor data and current image processing techniques to improve mushroom farming conditions. NTM-based mushroom cultivation is more efficient and yields more than conventional methods.

关键词： Temperature measurement Temperature sensors support vector machines Accuracy smoothing methods Turing machines soil measurements soil moisture streaming media Robot sensing systems

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Investigation of a Novel schiff base;Catena-((µ6 -(E)-2-((4-methoxy-2-oxidobenzylidene)ammonio)ethane-1-sulfonato potassium as a potential antibacterial agent, synthesis, spectral and Hirshfeld surface Analysis, Crystal structure, DFT Calculations

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ssRN 2024年

作者： Yılmaz, sümeyye Kırkıncı Aşkar, Şinasi Poyraz, Emine berrin Dege, Necmi Ağar, Ayşen Alaman sidden, Chinnasami Department of Chemistry Faculty of Science Ondokuz Mayıs University SamsunTR-55270 Turkey Department of Nutrient and Dietetics Çankırı Karatekin University ÇankırıTR-18200 Turkey Department of Physics Faculty of Science Ondokuz Mayıs University SamsunTR-55270 Turkey Department of Physics V.S.B Engineering College Tamil Nadu Karur639111 India

A new water soluble schiff base complex, Catena-((µ6-(E)-2-((4-methoxy-2-oxidobenzylidene) ammonio) ethane-1-sulfonato potassium, C10H14KNO6s, (CMOAEsP) was synthesized by a one step condensation reaction of 2-hydroxy-4-methoxy benzaldehyde and 2-aminoethane sulfonic acid with the yield of %65, 0,333 g. The CuIMDsCH compound was characterized with spectroscopic, DFT and Hirshfeld surface analysis methods. spectral analysis of water soluble crude product showed equilibria keto-enol tautomerism , by a resulting Zwitterionic form of the structure. X-Ray analysis of the structure was in a good agreement with the spectral investigation of the *** ADMET research of the structure showed, the CMOAEsP obeys the Lipinski’s rule and the compound was in bioavability range. The molecular docking results showed an inhibition ability for 1JIJ protein which plays an important role in antibacterial studies. The experimental end theoratical calculations were in a good agreement that CMOAsP has inhibition efficiency against Escherichia coli and has inhibition at high concentrations against staphylococcaceae aureus. © 2024, The Authors. All rights reserved.

关键词： Ethane

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Metaheuristic Algorithm for Automatic Cruise Control system 3

Metaheuristic Algorithm for Automatic Cruise Control System

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3rd International Conference on smart Electronics and Communication, ICOsEC 2022

作者： Prasanna, v. Nelson, A. Hnaumanthakari, sudheer Kumar, v. Kamal Kirubakaran, s. Kumar, M Jogendra B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Tamil Nadu Vandalur600048 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Faculty of Science and Technology Icfai Foundation for Higher Education Department of Electronics and Communication Engineering Telangana Hyderabad501503 India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai641407 India Cmr College of Engineering and Technology Department of Computer Science and Engineering Telangana Kandlakoya501401 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Accidents are less likely to happen when employing self-driving cars. However, automotive applications require more time and cost, simulation tool is a much more cost-effective and time-saving option. To reduce driver fatigue and ensure pedestrian safety, a vehicle's cruise control depends heavily on control engineering. To aid in vehicle safety, the automotive manufacturing sector is forecast to develop a range of cruise control strategies. because of elevated traffic on the roads, there is a significant need for the development of Automatic Cruise Control (ACC). The feedback control system's architecture is essential for the achievement of ACC. This paper presents the controller tuning techniques for designing an ACC system. Researchers frequently utilized Ziegler-Nichols techniques in tuning Proportional Integral Derivative (PID) controllers. These tuning processes may be time-consuming and difficult for a complex nonlinear structure. This paper shows how to employ metaheuristic methodologies to quickly fine-tune the PID controller's parameter. The first stage is to construct a mathematical model of the cruise control system on the vehicle. The PID settings are then optimized using the firefly and cuckoo search optimized algorithm. both metaheuristic PID controllers achieve the necessary vehicle velocity, according to simulations. From the results, it also inferred that the designed controller helps the system to attain the required velocity smoothly, with negligible overshoot and minimal rise time. © 2022 IEEE.

关键词： Optimization

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Advanced Fake News and Hate speech Detection Leveraging spectral Graph Theory

Advanced Fake News and Hate Speech Detection Leveraging Spec...

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Distributed systems, computer Networks and Cybersecurity (ICDsCNC), International Conference on

作者： M sowjanya K. M. Devendraiah v. Kalpana K. venkata Ramana bramah Hazela Karthik Kumar vaigandla Department of ECE Geetanjali College of Engineering and Technology Cheeryal India V. V. Sangha's Independent P U College Ballari India Department of English R. M. K. College of Engineering and Technology Puduvoyal India Vardhaman College of Engineering Kacharam India Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida India Department of Electronics and Communication Engineering Balaji Institute of Technology and Science Warangal India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Despite the proliferation of the Internet, prejudice and misinformation have persisted throughout history. False news and hate speech have long been a part of human nature, as has lying and slander. The proliferation of social media, however, has changed the timing, location, and nature of disinformation and hate speech. False news and hate speech are no longer confined to offline forums thanks to the *** suggested method consists of preprocessing, feature extraction, and training the model. The impact of pre-processing was examined by analyzing various pre-processing procedures, which were subsequently followed by the suggestion of a combination of these strategies. We used Word2vec for feature extraction; it looks at a word's context to try to figure out what it means and how it's related to other words. Throughout the training process, spectral Graph Theory was regularly utilized. This cutting-edge method outperforms CNN and LsTM with an average accuracy of 94.95%.

关键词： Training Accuracy social networking (online) Hate speech Feature extraction Graph theory Timing Internet Fake news Long short term memory

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soil Mineral Prediction of Crops Using Machine Learning 3

Soil Mineral Prediction of Crops Using Machine Learning

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Akkasaligar, Prema T. biradar, sunanda Gudgeri, Manjula C. Mulla, sana Mohammadi A BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur586103 India Karnataka Vijayapur586103 India Kle Dr M S Sheshgiri College of Engg and Tech Department of Engg Science and Humanities Mathematics Karnataka Belagavi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

In India, agriculture is the primary source of income. Agriculture employs the majority of the people. because India's financial system is so reliant on agriculture, there is a pressing need to boost agricultural output generally. soil is the most important natural resource in agriculture. The most important requirement is good soil quality. The pH of the soil is crucial in this regard. The acidity of the soil is measured by its pH. The total yield is influenced by the pH level. Nitrogen, Potassium, and Phosphorus all influence pH of soil. Farmers can then begin cultivating crops as a result of this. pH sensors are commonly used to determine the pH of soil. However, because they increase the cost of manufacture, they are not always practicable. To address this problem, the proposed method determines the pH value while simultaneously recommending a crop. The crop recommendation method takes into account factors such as soil pH, season, and temperature. © 2022 IEEE.

关键词： Nitrogen

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Individual Privacy Protection in Cybersecurity Using Data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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Innovative Computing, Intelligent Communication and smart Electrical systems (ICsEs), International Conference on

作者： M. Arun bhavadharani K b. R. supreeth sumit Pundir K Pradeepa K. Abdul Razak Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamil Nadu India Department of Computer Science Oxford Degree and PG College Bangalore India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu Department of Mathematics K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), support vector Machines (svM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class svMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.

关键词： support vector machines Data privacy Data analysis Accuracy scalability Telecommunication traffic Data collection Privacy breach Protection computer security

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Cybernated Watermarking Approach on Image with Cryptographic Encryption

Cybernated Watermarking Approach on Image with Cryptographic...

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computer, Communication and Control (IC4), International Conference on

作者： R. sivasankari T veeramakali s M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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sharpened Cosine similarity U-Net for Deforestation Mapping 3

Sharpened Cosine Similarity U-Net for Deforestation Mapping

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3rd IEEE India Geoscience and Remote sensing symposium, InGARss 2023

作者： Jamali, Ali Roy, swalpa Kumar bhattacharya, Avik Lu, bing Ghamisi, Pedram Simon Fraser University 8888 University Dr W Department of Geography BurnabyBCV5A 1S6 Canada Alipurduar Government Engineering and Management College Department of Computer Science and Engineering West Bengal 736206 India Indian Institute of Technology Bombay Microwave Remote Sensing Lab Centre of Studies in Resources Engineering Mumbai400076 India Freiberg09599 Germany

ISBN: (纸本)9798350325591

Forests cover about 30% of the Earth's surface, having a significant global impact on climate and atmosphere. A significant change (i.e., forest loss) in the world's forests has been brought on by factors, such as rising population, increased urbanization, and environmental pollution due to economic activities. Consequently, forest cover and forest loss mapping and monitoring are vital. Convolutional Neural Networks (CNNs) are among the most utilized segmentation algorithms for deforestation mapping. However, CNNs may be more prone to increased model variance, over-sensitivity, and a lack of generalizability. Thus, new concepts, such as Cosine similarity can be investigated as an alternative approach to the current extensively utilized CNNs. Thus, in this study, we develop and propose the sCs-UNet for precise deforestation mapping utilizing satellite imagery of sentinel-2 in south America. The segmentation results illustrated that the proposed sCs-UNet segmentation algorithm exhibited the least training time complexity as compared to other implemented segmentation models, such as UNet, Attention UNet, R2UNet, ResUNet, swin UNet, UNet+++, TransUNet, and TransUNet++, while resulting in comparable statistical results as compared to the U-Net segmentation model. © 2023 IEEE.

关键词： Deforestation

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Enhanced Particle swarm Optimization based Node Localization scheme in Wireless sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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2022 International Conference on Augmented Intelligence and sustainable systems, ICAIss 2022

作者： singh, shashank Poonkuzhali, R. Nithya, G. Kumar, R. Anil Kartigeyan, J. Ramya, s. Department of Computer Science and Engineering Integral University Kurshi Road Uttar Pradesh Lucknow India Department of Information Technology K.S.Rangasamy College of Technology Tamilnadu Tiruchengode India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Telangana Secunderabad India Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Telangana Hyderabad India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu Thalavapalayam India

ISBN: (纸本)9781665489621

Wireless sensor networks (WsNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WsNs generally must be known. WsN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle swarm Optimization based Node Localization scheme (EPsONLs) in WsN. The presented EPsONLs technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPsONLs model applies the PsO algorithm to identify the node position. In addition, the EPsONLs model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPsONLs model over recent approaches. © 2022 IEEE.

关键词： sensor nodes

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Implimentation of Machine Learning to Detect Android Malware with significant Permission

Implimentation of Machine Learning to Detect Android Malware...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Murugadoss Department Artificial Intelligence and Data Science V.S.B. College of Engineering Technical Campus Coimbatore Tamil Nadu India

Malicious app proliferation has reached alarming levels, posing a severe risk to the developing digital ecosystem. According to the most recent study, a new dangerous Android app is created every ten seconds. Researchers need a modular malware detection system that can quickly locate and identify the most important bad programmes in order to stop this massive strike. There are many ways to identify malware, including system- and network-based techniques. On the other side, it is still difficult to expand detection for many different programmes. Researchers developed sigPID, a malware-finding method based on authorization usage analysis, to combat the fast increase in Android infections. Instead of gathering and evaluating all Android rights, this study proposes three rounds of pruning by mining permission data to locate the most significant permissions that can be helpful in differentiating between benign and harmful apps. Then, sigPID categorises numerous families of benign and malicious applications using techniques based on machine learning. Only 21 permissions, according to our analysis, are essential. The effectiveness of our approach, which only considers 23 rights, is then compared to a baseline technique that considers all permits. The results show that employing an svM as the classifier can lead to over 90% accuracy, recall, reliability, and F-measure, which are comparable to those discovered using the baseline technique, while having research that is 4–32 times less expensive than when using all rights. With a detection rate of 94 percent for viruses and 92 percent for unidentified malware specimens in the data, sigPID exceeds other cutting-edge methods.

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