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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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A Multi-channel Convolutional Neural Network for Multilabel Sentiment Classification Using Abilify Oral User Reviews

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Informatica (Slovenia) 2023年第1期47卷 109-114页

作者： Trueman, Tina Esther Jayaraman, Ashok Kumar Jasmine, S. Ananthakrishnan, Gayathri Narayanasamy, P. Department of Computer Science University of the People Pasadena United States Department of Information Science and Technology Anna University Chennai India Department of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India

Nowadays, patients and caregivers have become very active in social media. They are sharing a lot of information about their medication and drugs in terms of posts or comments. Therefore, sentiment analysis plays an active role to compute those posts or comments. However, each post is associated with multilabel such as ease of use, effectiveness, and satisfaction. To solve this kind of problem, we propose a multichannel convolution neural network for multilabel sentiment classification using Abilify oral user comments. The multichannel represents the multiple versions of the standard model with different strides. Specifically, we use the pre-trained model to generate word vectors. The proposed model is evaluated with multilabel metrics. The results indicate that the proposed multichannel convolutional network model outperforms the traditional machine learning algorithms. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Text-to-Sketch Synthesis via Adversarial Network

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computers, Materials & Continua 2023年第7期76卷 915-938页

作者： Jason Elroy Martis Sannidhan Manjaya Shetty Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya Department of Information Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia Department of Computer Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia School of Computing Skyline University CollegeUniversity CitySharjahUAE Department of Electronics and Communication Engineering SR UniversityWarangalTelanganaIndia Department of Computer Engineering–AI&BDA Marwadi UniversityRajkotGujaratIndia

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security ***,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch *** the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch *** this study,we propose an adversarial network that generates human facial sketches using such cues provided by an ***,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated *** evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance *** proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

关键词： Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques

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Automated Brain Abnormality Segmentation using Modified JAYA algorithm based Fuzzy C-Means Clustering 1

Automated Brain Abnormality Segmentation using Modified JAYA...

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

作者： Jeenath Shafana, N. Priya Lakshmi, S. Vijayalakshmi Ramani, U. Ramaraj, Kottaimalai Thilagaraj, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Tiruchirappalli India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350352931

The main risk factors in humans, irrespective of gender or location, are anomalies of the brain, such as tumors and strokes. In healthcare facilities, the degree of disability and underlying cause of brain lesions are typically evaluated by means of regular methods created by trained clinicians. During this procedure, signals and images of the brain are collected in a controlled setting. Selecting an appropriate separation method is essential to getting the desired results. The novel JAYA algorithm, which relies on meta-heuristics, aids in the reclustering of the FCM segmentation results in our suggested techniques, yielding more productive delineation results with clear borders with 86% of Dice Score. However, the exploitation and exploration techniques for searching become more flexible and help identify the optimal solution faster by adjusting the desired function with respect to the accommodating cluster location and population size. Regarding the processing speed, optimal exploitation, and exploratory abilities, the suggested strategy outperforms the other available metaheuristic optimization techniques. © 2024 IEEE.

关键词： Image segmentation

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OCSO-CA:opposition based competitive swarm optimizer in energy efficient IoT clustering

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Frontiers of computer Science 2022年第1期16卷 77-87页

作者： Arpita BISWAS Abhishek MAJUMDAR Soumyabrata DAS Krishna Lal BAISHNAB Department of Electronics and Communication Engineering National Institute of Technology SilcharSilchar788010India Department of Computer Science&Engineering Karunya Institute of Technology&SciencesCoimbatore641114India Department of Electrical Engineering National Institute of Technology SilcharSilchar788010India

With the advent of modern technologies,IoT has become an alluring field of *** IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy *** this respect,this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and ***,a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency,network lifetime and ***,an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device *** proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient ***,a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed.

关键词： competitive swarm optimization cloud computing clustering IoT

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Integrating AI, IoT, and Drones for Sustainable Apple Orchard Monitoring in Society 5.0 2nd

Integrating AI, IoT, and Drones for Sustainable Apple Orch...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Datta, Ankana Paul, Sukalpa Pal, Anidipta Biswas, Sounav Bag, Anil Kumar Sengupta, Diganta Department of Computer Science and Engineering - AIML Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India

ISBN: (纸本)9783031845420

Sustainable smart agriculture forms one of the focal points in Society 5.0. We propose an AI-IOT enabled framework capable of processing multi-modal data for apple orchard monitoring. Real-time data acquisition is done from ground sensors as well as drone fitted cameras. The ground sensors monitor soil moisture, pH levels, nutrient composition (nitrogen, phosphorous, and potassium), ambient temperature, and humidity, while ESP32-CAMs fitted on drones capture images of apples, leaves, and trees. IOT enabled Unmanned Aerial Vehicle (UAV) as well as the ground sensor framework feed the data to the cloud. YOLOv8 and ResNet152 have been used to process the images for classifying the health of the apple plants. Machine learning models predict the farm yield using the ground sensor data reflecting the soil conditions. Our framework fares better than prior art in terms of accuracy. Although existing literature exhibits processing of soil data for prediction of health and yield, our study takes into consideration three further nutrient components - nitrogen, phosphorous, and potassium. Our study shows accuracy of 98.19% (apple counting) 53.66% (apple classification) 96.00% (leaf classification). To the entirety of our understanding, this study is the first to use multi-modal data inclusive of extended soil nutrients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orchards

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LeNet with Bidirectional Long Short-Term Memory for Signal Detection in Underwater Optical Communication 1

LeNet with Bidirectional Long Short-Term Memory for Signal D...

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

作者： Subhashini, S. Al-Farouni, Mohammed Kumar, B. Satish Sharmiladevi, D. Anita Sofia Liz, D.R. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Maisammaguda Department of Electronics and Communication 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 Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. Due to complex underwater environment the high directionality of light beam and vibration transceiver incident optical power usually spans a very wide dynamic range. This paper, proposed LeNet with Bidirectional Long Short-Term Memory (BiLSTM) technique performed efficiently in the signal detection in under water optical communication by effectively extracting spatial features and capturing temporal dependencies. The data collected for this research includes data bits of 72 vectors of transmitted signals through the Gulf seawater. The LeNet approach process the received optical communication signals and extract spatial features from convolutional layer, converting them into relevant signal to enhance the communication of underwater. The combination of LeNet feature extraction capabilities and Bi-LSTM sequential learning the signal then enhances the model ability leading to achieve better performance in signal detection. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall, f1-score and Bit Error Rate (BER) metrics based on sea water. © 2024 IEEE.

关键词： Error statistics

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics 4

Proactive Network Optimization Using Deep Learning in Predic...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Priya, S. Anu Bhat, Niyati Kanna, B. Rajesh Rajalakshmi, S. Jeyavathana, R. Beaulah Srimathi, S. Department of Computer Applications Chennai600117 India University of Mumbai B.E Computer Engineering Mumbai India Alagappa Chettiyar College of Engineering and Technology Department of Electronics and Communication Engineering Karaikudi India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering India Srm Institute of Science and Technology Computational Intelligence Kattankulathur Chennai India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350307757

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era. © 2024 IEEE.

关键词： Time series analysis

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Wearable Saudi Sign Language Recognition Device Based on Neural Network 21

Wearable Saudi Sign Language Recognition Device Based on Neu...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Algahtani, Shouq Zuhairy, Rana Almarghalani, Olla Aljohani, Mawadda Elmanfaloty, Rania A. King Abdulaziz University Department of Electrical and Computer Engineering Jeddah Saudi Arabia Alexandria Higher Institute of Engineering and Technology Department of Electronics and Communication Engineering Alexandria Egypt

ISBN: (纸本)9798350374131

In Saudi Arabia, over 750,000 individuals are deaf or mute, often facing communication barriers due to limited sign language familiarity among the general population. This research introduces a wearable device leveraging neural networks to recog-nize Saudi Sign Language, aiming to bridge this communication gap. The device simultaneously processes data from both hands, integrating inputs from the non-dominant hand with those from the dominant. Three primary phases define the system: data acquisition, machine learning model development, and real-time sign recognition. Data were gathered using electromyography (EMG) and inertial measurement unit (IMU) sensors, facilitated by the Arduino IDE, with two Arduino Nano 33 BLE micro-controllers managing collection and transmission. The dataset, comprising 25 signs from four subjects with each sign repeated 20 times, informed the machine learning process. Utilizing Python, a model incorporating dual Convolutional Neural Networks for feature extraction from sensor readings and dense neural networks for training was built. Subsequent sign recognition was realized by deploying the model on Arduino via TensorFlow Lite. Impressively, this innovative system boasts a 92 % accuracy rate, marking a significant stride toward inclusive communication solutions. © 2024 IEEE.

关键词： Data acquisition

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Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation 3

Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wil...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Saravanan, S. Raj, J. Relin Francis Rajkumar, G. Vinoth Kumar, C. Antony Vasantha Soundiraraj, N. Bharathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (纸本)9798331532420

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management. © 2024 IEEE.

关键词： Decision making

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