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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. Dinesh, P.M. Jeyageetha, K. Lakshmi, G. Prasanna Thangavel, Kuntavai Alagarsamy, Manjunathan Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Ramco Institute of Technology Department of Computer Science and Engineering Tamil Nadu Rajapalayam626117 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Adhiparasakthi Engineering College Melmaruvathur Department of Electrical and Electronics Engineering Tamil Nadu Chengalpattu603319 India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350390049

A bacterial infection is the primary cause of the chronic lung disease tuberculosis, among the ten most common causes of death. Because it may be fatal, TB must be accurately and promptly detected. The identification and treatment of the problem caused by TB require the accurate detection of the disease using image pre-processing, Deep learning, picture segmentation, and data augmentation categorization using X-ray pictures of the chest. Before being employed for transfer learning, the initial weights of nine distinct deep CNN's (ResNet18, ResNet50, ResNet101, Chex Net, InceptionV3, Vgg19, DenseNet201, Squeeze Net, and Mobile Net) were trained, validated, and tested for TB and non-TB case detection. © 2024 IEEE.

关键词： Image segmentation

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Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

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computer Systems Science & engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan S.Ramesh S.Janupriya Sangsoon Lim Seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer Science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data Science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated *** proposed CSM-FFDNN model involves two major processes namely FE and *** proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

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Interpretation of Sign Language Using Machine Learning 2

Interpretation of Sign Language Using Machine Learning

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Srivastava, Palak Hazela, Bramah Singh, Shikha Asthana, Pallavi Pandey, Deependra Singh, Kamlesh Kumar Singh, Vineet Amity University Amity School of Engineering & Technology Department of Computer Science Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Department of Electronics & Communication Engineering Uttar Pradesh India

ISBN: (纸本)9798350364699

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments. © 2024 IEEE.

关键词： Adversarial machine learning

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A Patient Response Prediction System using Multi-Scale Feature Fusion Semantic Segmentation Network and Med-YOLO 1

A Patient Response Prediction System using Multi-Scale Featu...

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

作者： Shankari, S. Uma Ali, Hassan Mohamed Durga, Boya Vijaya Vadivu, M. Senthil Mangaiyarkarasi, V. Srm Institute of Science and Technology Ramapuram Department of Computer Science and Applications Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Maisammaguda School of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, most of the people are getting affected with various health issues such as typhoid, diabetes and cancer. A human immune system plays a crucial role in maintaining and balancing human health and reduces the risk of getting ill. Immunotherapy is a treatment which utilizes human body immunity power to fight cancer. There are many challenges in the existing models such as imbalance data, heterogenic data, uncertainties, and lack of temporal data. These challenges are resolved by imposing deep learning techniques and generating a valid information with the clinical details of patient health conditions. Therefore, a hybrid model name called Multi-Scale Feature Fusion Semantic Segmentation Network (MSSNet) and You Look Only Once (MedYOLO) for feature extraction and detection. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is employed to overcome the issue of imbalanced data;further MSSNet is imposed for extracting the optimal features and detected using MedYOLO. Also, to get detailed information and generate cohort, the data collected from GSC hospital with overall details that are essential to predict the patient response to immunotherapy in cancer treatment. In the end, MSSNet-MedYOLO obtained higher accuracy of 98.16% for an effective prediction of patient response which is better than existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Semantic Segmentation

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A Novel Sharding Protocol for Blockchain Scalability Using Random Function Assignment 3

A Novel Sharding Protocol for Blockchain Scalability Using R...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Ellappan, V. Sungheetha, Akey Vetrivendan, L. Mahapatra, Sheila Sivakumar, C. Alliance University Department of Computer Sciences and Engineering Bengaluru India Mahendra Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798331518592

Blockchain technology has a major barrier to its expansion, which is scalability. However, current methods have problem of security vulnerabilities and inefficiencies and although sharding has proved to be a good mitigation for this problem, there is no actual implementation of it in any relational database. Our paper explores the improvements to the sharding protocol by integrating the Verifiable Random Functions (VRF) with a feature of reshuffling for optimal distribution on the chain. Verification also showed that our solution provided a transaction through rate that is forty percent higher than the current existing shard handling protocols with only 0.3% error margin on transaction validation standing at 99.7%. Nonetheless, the system effectively addressed the goal of minimizing cross-shard communication by a ratio of 35% though was able to enhance the mean load balancing efficiency by only 28%. Interestingly, our protocol enhances the defense against the takeover of shard from an opponent, an aspect that makes the protocol ideal for large scale application in BFSI and SCM applications. Our results show not only the scalability extension of VRF-based sharding but also the strong security for blockchain system, making sharding a feasible fully-solution for the current enterprise-level blockchain concerns. © 2024 IEEE.

关键词： Distributed ledger

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Analytical Model for Performance Measures over Respiratory Track 27

Analytical Model for Performance Measures over Respiratory T...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Jaiswal, Raghavendra Singh, S. Pratap Gulhane, Monali Rakesh, Nitin Singh, Rajneesh Kumar Galgotias College of Engineering and Technology Department of Electronics and Communication Engineering Greater Noida India Department of Computer Science and Engineering Pune India Sharda University Sharda School of Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Diffusive Molecular Communication (DMC) as a framework for modeling bacterial propagation within biological environments. DMC, a subset of Molecular Communication (MC), allows us to study how bacteria transmit signals inside the human body. The effectiveness of the model depends on the chosen deployment geometry. We focus on a cylindrical biological deployment inspired by the human respiratory tract. This model considers the degradation and flow of information molecules diffusing through the fluid medium. Furthermore, proposed statistical models for bacterial concentration f(y) Further, analyse the key performance metrics within this CBD model and present statistical models for bacterial propagation are binding probability (Pb), non-binding probability (Pnb), evade probability (Pe(y)), and the probability of binding rate (Pb(y)). Major contribution of numerical analysis to find total number of receptor bacteria at y segment N(y), and Bacterial distribution V(y). Further, the numerical results demonstrate excellent agreement with the theoretical foundation. © 2024 IEEE.

关键词： Bacterial distribution binding probability cylindrical biological deployment Diffusive Molecular Communication

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Emotion Recognition from EEG Using Mutual Information Based Feature Map and CNN 1st

Emotion Recognition from EEG Using Mutual Information Based ...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Maria, Mahfuza Akter Hossain, A. B. M. Aowlad Akhand, M.A.H. Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Electronics and Communication Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (纸本)9783031346217

Emotion is the fundamental trait of human beings, and brain signal is the most prospectus for emotion recognition. Electroencephalography (EEG) is a preferable brain signal for recognizing emotions. Extracting features from EEG signals is an essential part of this emotion recognition. Recently, EEG channel connectivity features have been widely used in emotion recognition. Automatic emotion recognition (ER) from EEG is a challenging computational intelligence or machine learning task due to the inherited complexity of EEG signals. The aim of the study is to analyze mutual information (MI) based connectivity measures for feature extraction from EEG signals and classify emotions using deep learning. At a glance, this study investigated MI and its two variants, Normalized MI (NMI) and Partial MI (PMI), for connectivity feature extraction from EEG;and then Convolutional Neural Network was employed for emotion classification from these features. Experimental results confirm the effectiveness of PMI based ER method while compared with related methods on a benchmark EEG dataset. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Feature extraction

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Design and Circuit-Level Assessment of Memristor-NMOS for Low-Power Applications 2

Design and Circuit-Level Assessment of Memristor-NMOS for Lo...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Tejesh, B.S.S. Tripathy, Manas Ranjan Ramakrishnan, M. Darshini, K. Mariya Priya Sreenivasulu, Vakkalakula Bharat Singh, Ashish kumar Department of Electronics and Communication Engineering SRM University Andhra Pradesh Amaravati India School of Electronics Engineering KIIT University Bhubaneswar India Department of Electronics and Communication Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India Department of Electronics and Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Bengaluru India Department of Computer Science Engineering Chitkara University Institute of Engineering and Technology Chitkara University Patiala India

ISBN: (纸本)9798331504960

This manuscript presents an essential overview of the implementation of memristor-based logic circuits. Different memristor models such as Linear Drift, Simmons Tunnel, Yakopcic, Non-Linear ion Drift, TEAM, and VTEAM have been taken into consideration and a comparison of results has been done adopting all these models. The logic gates such as NAND, NOT, NOR, AND, OR along with some combinational circuits such as a 1-bit comparator, and 2x1 multiplexer were designed and implemented using MRL (Memristor Ratioed Logic) in the LTSpice simulation tool. The primary target of this paper is affirming the attributes of memristor at the device level. Here the logic circuits are implemented using Memristor and NMOS devices. All the circuits designed in this paper operate at a supply voltage of 1V using HP memristor model. Moreover, the power consumed by the designed circuits has been analyzed and found to be in the order of pW. © 2024 IEEE.

关键词： Logic gates

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Supply Chain Planning of Agricultural-Products Under Water Scarcity Using Deep Learning

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Iranian Journal of Science and technology - Transactions of Civil engineering 2025年 1-14页

作者： Sampath, P. Mahariba, A. Jackulin Sathiyamurthi, P. Sridharan, S. Department of Computer Science and Engineering - Cyber Security Easa College of Engineering and Technology Tamil Nadu Coimbatore India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electrical and Electronics Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

All people on the planet expected agriculture to provide them with food to survive. The process of producing agricultural goods while integrating logistical elements is labor-intensive. Additionally, the lack of water exacerbated several issues with agricultural product production. The researchers did not adequately address the issues surrounding the lack of water over food chains. For supply chain management, this paper presented a novel deep learning based optimization mechanism to address the agricultural production challenges under water scarcity. Hence, this study proposed a Dynamic Salp Swarm Optimization (DSS) algorithm for feature selection. The DenseNet121 based Modified Runge–Kutta Optimization (MRKO) is used to predict the water usage. For supply chain optimization, unique technique called Beetle Swarm Optimization (BSO) proposed. In addition, this reduces production costs and time while increasing profitability. The Python simulator is used to perform simulations and implement the processes. According to the similar study, the proposed strategy outperforms all other methods in terms of supply chain manufacturing cost, time, accuracy, and recall for water consumption predictions. The overall sustainability enhanced and the agricultural supply chain water scarcity impact mitigated with these findings and it describes the potential of proposed work compared to state-of-art. From the comparative study, the proposed work demonstrated 96.81% accuracy, 95.7% precision and 96.8% recall with3.11% MSE, 2.10% MAE, and 3.10% RMSE results. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Agri-products Supply chain Water consumption and DenseNet121 Water scarcity

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