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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Justina Michael, J. Thenmozhi, M. Department of Computer Science and Engineering SRMIST Kattankulathur campus Chennai India Department of Networking and Communications SRMIST Kattankulathur campus Chennai India

ISBN: (纸本)9789819951659

Soybean is one of the highest protein diets which is consumed globally. It is also used for producing animal feed, biodiesel, crayons, bio-composites, carpets, candles, cleaners, ink, tires, foam, and way more. However, from sowing to harvesting soybean, several difficulties emerge among which weed removal is most challenging. To assist agriculturalists, several technologies have evolved. Robotic weeding is one such technology that eradicates the growth of weeds without human intervention thereby multiplying the crop yield. This paper focuses on building the best soybean crop and broadleaf weed classifier that helps robots in weed identification and removal by differentiating crops from weeds. Using transfer learning, 16 different CNN-based architectures were built and compared to determine the best classifier. 2382 observations (images) were used to build these classifiers. To expand this crop-weed dataset, six augmentation methods were applied to the training images. 24 metric values were computed for every classifier for comprehensive result analysis. Top-3 metrics were selected and analyzed for finding the best classifier. Ultimately, MobileNet outperforms all other classifiers by resulting in 99.58% model accuracy. Results are tabulated, and graphs are represented for understanding the outcome precisely. Future works in weed identification and removal were also discussed before concluding. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Improving Biometric Security Using PulsePrint: Real-Time Defense Against Fingerprint Spoofing 2

Improving Biometric Security Using PulsePrint: Real-Time Def...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Vigneshwaran, P. Maranco, M. Ivaturi, Kashyapa Abhiram Takur, Purab Acharya University Department of Computer Science and Engineering Karakul Bukhara Uzbekistan SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Chengalpattu Kattankulathur603203 India

ISBN: (纸本)9798350365269

Biometric authentication systems have become increasingly popular due to their convenience and perceived security benefits. However, traditional biometric modalities such as fingerprint recognition are vulnerable to spoofing attacks, where fake fingerprints can be used to deceive the system. In this paper, we propose PulsePrint, a novel biometric authentication solution that enhances security by incorporating real-time pulse detection alongside fingerprint recognition. By leveraging the unique pulsatile nature of blood flow in the fingertip, PulsePrint adds an additional layer of authentication, making it significantly more difficult for attackers to spoof fingerprints. We present the design and implementation of the PulsePrint system, detailing the integration of pulse detection algorithms with existing fingerprint recognition technologies. Furthermore, we evaluate the performance of PulsePrint through extensive experimentation, including testing against various spoofing techniques and benchmarking against conventional fingerprint recognition systems. Our results demonstrate that PulsePrint offers robust protection against fingerprint spoofing attacks while maintaining user convenience and real-time authentication speed. Overall, PulsePrint represents a promising advancement in biometric security, offering enhanced protection for applications requiring reliable and secure authentication. © 2024 IEEE.

关键词： Authentication

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Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 2337-2351页

作者： S.M.Udhaya Sankar Mary Subaja Christo P.S.Uma Priyadarsini Department of Information Technology Velammal Institute of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Energy conservation has become a signiﬁcant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain *** a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efﬁcient way remains a ***,the clustering strategy employs to enhance or extend the life cycle of *** identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the ***,the critical issue was determining the best SH for ensuring timely communication *** secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize *** sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data *** modeled our methods to accom-plish power-efﬁcient multi-hop ***,protected navigation helps to preserve energy when *** suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

关键词： Wireless sensor network wireless security wireless routing clustering ad hoc network

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Contagion Classification using Deep Learning Approach 2

Contagion Classification using Deep Learning Approach

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Kacham, Sai Prajwal Angayarkanni, S.A. Rohithshan, S. Mishra, Arpan Vignesh, K. SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communication Tamilnadu Kattankulathur603203 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil626126 India

ISBN: (纸本)9798350365269

Potatoes are a major crop globally, but diseases can seriously harm their growth and quality. Recently, there's been a lot of interest in using machine learning to identify potato diseases from pictures. In this study, we've developed a way to automatically classify potato diseases using deep learning methods. By training a model with CNN and SVM algorithms on a dataset of potato disease images, we achieved an accuracy rate of 93.95%. This shows that deep learning is effective in identifying potato diseases. Our method could be a big help to farmers and researchers, making it faster and easier to spot and deal with potato diseases, which could lead to better crop yield and quality. © 2024 IEEE.

关键词： Support vector machines

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Intense Triad Defender for End-User Security in Cyber Physical System 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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Transforming Lung Carcinoma Analysis: Deep Learning's Efficient Segmentation and Classification

Transforming Lung Carcinoma Analysis: Deep Learning's Effici...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Divya, D. Saveetha, D. Bharathi, V.C. Shanmugam, P. Jerusalem College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

Cancerous cells can develop in the lungs, a condition known as lung carcinoma. One of the most deadly and challenging malignancies to cure, lung cancer often spreads to other parts of the body at an early stage. The World Health Organisation reports that for several decades, lung cancer has topped all cancers globally in terms of incidence. Early detection and treatment of lung cancer are of the utmost importance. More effective treatments, less intrusive surgeries, and longer survival times may result from this. This paper presents a method for efficiently segmenting and classifying lung carcinomas using convolutional neural networks (CNNs) for the purpose of identifying malignant tumors. Here, additional effective methods such as an adaptive median filter for preprocessing, histogram equalization, edge-based segmentation, and Convolutional Neural Network (CNN) for successful classification are used to identify the lung carcinoma. To make segmentation more efficient, we use the Adaptive Median Filter in conjunction with the Histogram Equalization technique. It is also possible to transfer the important properties to the classification algorithm by deriving them from feature similarity. When compared to another prediction model that is already in use, the suggested model has the potential to attain superior accuracy. Reduced time and effort spent on detecting lung cancer are two benefits of the enhanced performance. © 2023 IEEE.

关键词： classification Convolution Neural Network Histogram Lung carcinoma Median Filter

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An Efficient Dilated DTCN with Residual LSTM-based Allergenicity Prediction for Chemical Compounds using Optimal Weighted Feature Selection and Improved Optimization Mechanism 2

An Efficient Dilated DTCN with Residual LSTM-based Allergeni...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Padmavathy, T. Thenmozhi, M. Madhavan, Thirumurthy SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Genetic Engineering Chennai India

ISBN: (纸本)9798350365269

Allergy is the sudden response of the immunity system which arises later the disclosure of allergens like chemicals, proteins and peptides. In the early times, multiple techniques were formed for allergenicity forecasting of peptides and proteins. However, there is no technique to anticipate the chemical and potential allergenic. The standard food allergen recognition mainly depends on vitro and vivo research, which frequently needs more time and is expensive. Artificial Intelligence (AI) operates a quick food allergen detection model, which solves the above-defined problems and becomes an effective auxiliary tool. Evaluation of potential allergenicity of protein is required at any movement of transgenic proteins implemented in the food chain. Therefore, it is necessary to solve more complications involved in the conventional allergenicity prediction technique for chemical compounds. Thus, a new allergenicity prediction framework for chemical compounds is designed using deep learning techniques. Initially, essential allergenicity data are accumulated from the standard measures. Next, the acquired data are presented for the optimal weighted feature selection stage. In this phase, the weights and features are selected optimally using Opposition Tasmanian Devil Optimization (OTDO). Further, the adopted optimal weighted features are offered to the allergenicity forecasting stage. Here, the allergenicity is predicated on using the developed "Dilated Deep Temporal Context Networks with Residual Long Short-Term Memory Networks (DiDNet-ResLSTM)". Hence, the recommended allergenicity prediction model effectively higher performance rate in different experimental observations. © 2024 IEEE.

关键词： Forecasting

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Optimizing Forest Fire Detection Systems: A Deep Learning Approach with UAVs and Binary Sand Cat Swarm Optimization 3

Optimizing Forest Fire Detection Systems: A Deep Learning Ap...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, T. Rajesh Devi, K. Komala Subramanian, P. Mahaveerakannan, R. Balamanigandan, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering India Cmr Institute of Technology Department of Ise Bengaluru India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Networking Institute of Cse India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Devastating forest fires are an international environmental crisis that endangers both people and their valuable natural resources. Forest fires are becoming more common and more destructive, leading to significant depletion of natural resources. A combination of human interference with natural processes and the effects of climate alteration have contributed to a recent uptick in the frequency of wildfires. Combating this issue requires firefighters to respond swiftly and identify fires from early smoke so they can prevent the fires from spreading. An efficient system for detecting and monitoring fires is essential for reducing this risk. The purpose of this study is to investigate and assess the state of the art in forest fire detection and monitoring with the use of deep learning algorithms and drones or unmanned aerial vehicles (UAVs). An efficient and economical way to monitor in real-time and detect fires early is to use drones that are outfitted with specialised sensors and cameras. Using the suggested customised Convolutional Neural Network models, the datasets were tested. Deep Q learning (DQL) is used to extract features, which enhances the accuracy of classification. When compared to other transfer learning methods, such as VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, besides Inception V3, their outcomes are evaluated. To analyse and solve the optimal parameter selection problem, the authors suggest using Binary Sand Cat Swarm Optimisation (bSCSO) to fine-tune the parameters of the prototypical. The suggested custom VGG16 model outstripped state-of-the-art models by a significant margin (98.14%), which was measured by its ability to generalise to unknown images. © 2025 IEEE.

关键词： Drones

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