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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sangeetha, K. Vishnu Raja, P. Siranjeevi, S. Suman, J. Rohith, S. Kongu Engineering College Faculty of Computer Science and Engineering India Kongu Engineering College Cse Department India

ISBN: (纸本)9798350385649

Fruit classification is an indispensable component of the modern world, with applications ranging from agriculture and food production to retail and distribution. Accurate classification of fruits ensures quality control and helps in streamlining supply chains. However, fruit classification is a complex endeavor, primarily due to the intrinsic diversity of fruits in terms of size, shape, color, and other characteristics. The challenge intensifies when the goal is not only to identify fresh fruits but also to detect and classify rotten or spoiled ones. The existing models and systems designed for fruit classification have been proficient in categorizing fresh, visually appealing fruits. These models have found widespread utility in industries such as agriculture and supermarkets, where the goal is to separate fruits that meet certain quality standards. However, they fall short when it comes to addressing the critical issue of identifying and classifying fruits that are no longer fit for consumption, which is equally important to prevent waste and maintain quality control. To bridge this gap, this project develops a comprehensive approach. It begins with the acquisition of a dataset that includes both fresh and rotten fruits. By combining the power of deep learning, specifically Convolutional Neural Networks (CNN), the project aims to classify fruits into distinct categories. The CNN model is trained to differentiate between fresh and rotten fruits by learning from a diverse set of images. In addition to classification, the project employs the capabilities of OpenCV, a popular computer vision library, to assess the ripeness of fruits based on the color. OpenCV provides a robust platform for analyzing color variations in fruit images. By leveraging this color analysis, the project can not only classify fruits but also determine their ripeness levels, providing a more holistic evaluation of fruit quality. The integration of CNN -based classification and OpenCV-driven ri

关键词： Convolutional neural networks

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Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs

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computer Systems science & engineering 2023年第1期44卷 99-112页

作者： D.Loganathan M.Balasubramani R.Sabitha S.Karthik Department of Electronics and Communication Engineering Info Institute of EngineeringCoimbatore641107India Department of Computer Science and Engineering SNS College of TechnologyCoimbatore641035India

Sensors are considered as important elements of electronic *** many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop ***,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much ***,reducing energy con-sumption can increase the network lifetime in effective *** that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor *** that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member *** providing equal rate of energy consumption among nodes,the dimensions of framed clusters are ***,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base *** evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head ***,the model also provides higher throughput and minimal delay in delivering data packets.

关键词： Wireless sensor networks energy consumption load balanced clustering finest routing

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Optimization of Strength Properties of Reactive Powder Concrete by Response Surface Methodology

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Journal of Shanghai Jiaotong university(science) 2024年第5期29卷 900-908页

作者： SAKTHIESWARAN N. MOORTHY N. RENISHA M. CHINNADURAI M. Department of Civil Engineering E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Science and Humanities E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Computer Science and Engineering E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia

The main objective of this study is to optimize the fresh and strength properties of reactive powder concrete incorporated with industrial by-products like ultra-fine ground granulated blast furnace slag as cement substitute and added with coal bottom ash and recycled concrete fines as partial replacement of quartz sand by response surface methodology through design of experiment *** four responses namely slump,compressive strength(C-28),flexural strength(F-28),and split-tensile strength(S-28)after 28 days of curing period were *** statistical study on the reactive powder concrete includes the modeling of regression,normal probability plots,surface plot analysis,and optimization of process *** regression models of the considered responses(slump,C-28,F-28,and S-28)were *** results obtained from the analysis of variance(ANOVA)and Pareto chart were used to determine the statistical significance of the process *** influence of the variables on the responses was studied by means of the surface plot *** optimal proportion of the variables against the responses was obtained through optimization *** resulted regression equations were in the form of second-order polynomial equation and the prediction of strength properties was found to be in line with the experimental *** difference of proportion of variance indicated that only 0.43%,6.42%,5.15%,and 9.7%of deviations cannot be expressed by the *** ANOVA and Pareto charts represented the high significance and appropriateness of the linear term of slump response and the two-way interaction term of strength *** results of the optimization response revealed the optimal proportions of recycled concrete fines and coal bottom ash as 19.15%and 7.02%,respectively.

关键词： reactive powder concrete mechanical properties fresh properties statistics study coal bottom ash

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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images

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International Journal of Intelligent Networks 2024年第1期5卷 10-18页

作者： George, Neetha Shine, Linu N, Ambily Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering Rajiv Gandhi Institute of Technology Kottayam Kerala India Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Kerala India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Government Engineering College Wayanad Kerala India

The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases. © 2024

关键词： Optical tomography

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Brain Tumor Retrieval in MRI Images with Integration of Optimal Features

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Journal of Harbin Institute of Technology(New Series) 2024年第6期31卷 71-83页

作者： N V Shamna B Aziz Musthafa Department of Computer Science and Engineering PA College of EngineeringMangaluru 574153KarnatakaIndia Department of Computer Science and Engineering Bearys Institute of TechnologyMangaluru 5754199KarnatakaIndia

This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.

关键词： medical images brain MRI machine learning feature extraction and reduction content-based image retrieval(CBIR)

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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

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computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Optimized Long Short-Term Memory Using Hybrid Salp Swarm Optimization and Tabu Search for Medical Text Classification 10

Optimized Long Short-Term Memory Using Hybrid Salp Swarm Opt...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Yogameera, T. Shanthi, D. Theni Kammavar Sangam College of Technology Department of Computer Science and Engineering Theni India Psna College of engineering and technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350384369

Recently, many patients have sought treatment ideas through social media. The medical texts include a wealth of information, including a huge number of medical musculatures and symptoms. Developing an intelligence model to classify patient-provided text content and recommend the appropriate department is critical. Hence, an efficient medical text classification method is proposed using long short-term memory (LSTM) optimized by hybrid salp swarm algorithm (SSA) and tabu search (TS) algorithms called SSA-TS. The hybrid SSA-TS approach is used to optimize the hyperparameters of LSTM for enhancing the accuracy and convergence rate. The developed optimized LSTM approach is used to classify the patient-provided text. The experimental results conducted over health news in Twitter datasets and the performance of developed optimized LSTM are compared with some variants of LSTM methods. The experimental results reveal good generalization performance when compared to the baseline models on average, demonstrating that our model performs better and is more robust © 2024 IEEE.

关键词： Tabu search

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