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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nilahni, N. Sivasakthi, P. Kavitha, P. Madhumathi, S.M. Sakthi Lakshmi Priya, C. Saranyavaishalini, V.G. Department of Computer Science and Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798331527549

Growing plants in nutrition solution with any growing medium or roots dipped in distilled water is Hydroponics. Hydroponics, the practice of growing plants in a nutrient-rich solution without soil, offers significant advantages such as higher yield, reduced water usage, and efficient resource management compared to traditional farming. However, existing hydroponic systems often require separate monitoring and control platforms for different setups, such as Wick and Drip systems, increasing complexity and cost. This paper proposes a Combined Hydroponics Automation System utilizing IoT, integrating both Wick and Drip systems into a single, unified platform. The system employs Nodemcu ESP8266 to monitor key parameters such as pH, humidity, moisture, and water levels, with data transmitted to a cloud platform for real-time access and control. The unified approach reduces development and operational costs while simplifying the monitoring process. The outcome demonstrates improved efficiency in plant management, with streamlined data visualization, remote control, and optimized resource allocation, making it a cost-effective solution for urban and resource-limited farming environments. © 2025 IEEE.

关键词： Hydroponics

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Skin Cancer Detection Using Deep Learning 7th

Skin Cancer Detection Using Deep Learning

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Rakshit, Pranati Ghosh, Arundhati Chakraborty, Chirag Paul, Joydeep Das, Dinika Department of Computer Science and Engineering JIS College of Engineering Kalyani India

ISBN: (纸本)9789819764648

Skin cancer is characterized by the uncontrolled proliferation of abnormal cells in the outermost skin layer, the epidermis, due to unrepaired DNA damage leading to mutations. These mutations cause rapid multiplication of skin cells, forming malignant tumors. The primary types of skin cancer include basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma, and Merkel cell carcinoma (MCC). Melanoma of the skin ranks as the 17th most common cancer worldwide, with more than 150,000 new cases reported in 2020. Early detection and treatment of melanoma can significantly impact patient outcomes. The present work aims to detect melanoma skin cancer in its early stages using image processing through computer Vision and deep learning methodologies. The culmination of this effort is an Android application designed to facilitate self-diagnosis for users, offering timely alerts on when to consult a medical professional. Hospitals can also utilize the application to prioritize patient care based on their risk percentages, benefiting both patients and healthcare providers. The study delves into relevant research papers published in esteemed journals related to skin cancer diagnosis. Deep learning methods are proposed to assist dermatologists in achieving early and accurate diagnoses. While specialists can provide accurate diagnoses, the development of automated systems becomes crucial to efficiently diagnose diseases, saving lives and reducing healthcare and financial burdens. Machine learning (ML) emerges as a valuable tool in this context. The article focuses on the fundamentals of ML and its potential in aiding skin cancer diagnosis. The objective is to conduct a comparative study between the DenseNet-121, ResNet-50, and CNN-RF models. The study reveals that DenseNet-121 outperformed with a testing accuracy of 83%, surpassing ResNet- 50, which achieved 81% testing accuracy. This comparative analysis contributes to the ongoing research and development in the field of

关键词： Dermatology

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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Helmet and Equipment Detection with Worker's Mobility Tracker In Mining Sector Using YOLOv8 & LSTM

Helmet and Equipment Detection with Worker's Mobility Tracke...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Ramyadevi, R. Keerthy, S. Catherina J, Sherin Joys Dhayanithi, T. Saveetha Engineering College Department of Computer Science and Engineering Kanchipuram India

ISBN: (纸本)9798331529833

Monitoring personal protective equipment and compliance with safety procedures for mining operations is essential to ensuring personnel safety. Nevertheless, sending humans underground is impractical, as mining conditions are fast-changing and dangerous. Most of the existing systems are still not able to detect helmets, PPE, and the worker mobility in real-time, which are two critical aspects of safety. To overcome these challenges, this project bases its approach on enhancing monitoring and security by integrating machine learning technologies. Using YOLOv8, a cutting-edge object detection model, the system enables workers' helmet and PPE detection in real-time. This work also uses Long Short-Term Memory (LSTM), a special type of RNN, to track the behavior of workers within and over time. By reading the frames at 10 millisecond intervals, the system detects movement deviations be it unsafe movements or fatigue induced deviations and provides proactive measures for safety. The devised method reaches an outstanding accuracy of 95.9%, combining YOLOv8 and LSTM, which reduces human error and improves safety at the workplace. This state-of-the-art solution is not only providing continuous monitoring but is also increasing operational efficiency, thereby transforming safety solutions in the mining space. © 2025 IEEE.

关键词： Protective clothing

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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Brain Tumor Detection with Severity Identification Using K-Means Clustering 1

Brain Tumor Detection with Severity Identification Using K-M...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Deepa, B. Geetha, S. Sreesanth, S. Sridhar, B. Yuvarani, M. Nandha Engineering College Department of Computer Science and Engineering Erode India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518578

In the current investigation, we present a methodological framework that utilizes k-means clustering for the identification and measurement of cerebral neoplasms. The principal aim is to precisely as certain the existence of tumors within MRI scans and to evaluate the percentage of tumor involvement within the cerebral tissue. The proposed methodology initiates with the preprocessing of MRI images to enhance contrast and mitigate noise, thus augmenting the efficacy of subsequent analytical processes. Thereafter, k-means clustering is utilized to partition the MRI images into discrete clusters, with each cluster representing various tissue types within the brain. By leveraging the intrinsic intensity disparities between healthy and tumor-afflicted tissues, it becomes feasible to discern the clusters pertaining to tumor tissues. Subsequent to the segmentation phase, advanced post-processing techniques are implemented to refine the segmentation outcomes and extract significant features that are indicative of tumor presence and extent. These features encompass the dimensions, morphology, and spatial distribution of tumor regions within the MRI scans. Through a comprehensive quantitative analysis of these features, it is possible to estimate the percentage of tumor involvement in the brain, thereby furnishing invaluable insights for medical practitioners. Utilizing a dataset of brain MRI scans that encompasses both tumorous and healthy instances, the efficacy of the proposed methodology is evaluated. Furthermore, the estimated percentage of tumor involvement is juxtaposed against the ground truth annotations provided by medical professionals to authenticate the quantitative analysis. Overall, the suggested methodology demonstrates favorable results in the identification and quantification of cerebral tumors via the utilization of k-means clustering. By providing accurate assessments of tumor presence and the extent of involvement, the proposed technique possesses the cap

关键词： K-means clustering

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Energy Storage System Using Digital Twins with AI and IoT for Efficient Energy Management and Prolonged Battery Life in Electric Vehicles

Energy Storage System Using Digital Twins with AI and IoT fo...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Lakshmi, M. Sri Sarma, Vanam Ajay G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798331509828

This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehicles. Under typical operating conditions, the system exhibits predictive accuracy with mean absolute error relative to actual data equal to 0.042 and root mean square error equal to 0.055. These values reflect improvement in responsiveness of the system since latency has been reduced to 0.12 seconds with feedback response times established at 0.45 seconds. Moreover, it has increased by 15% in energy efficiency while promoting its longevity with a capacity fade rate measured at 0.0025% per cycle. Besides, the model shows strong anomaly detection capability - maintenance alert precision is found to be 95% while detection rate reaches76%. All these results point out the possibility that combining Digital Twin technology with AI and IoT may end up significantly boosting energy management systems for electric vehicles compared to more traditional approaches. © 2025 IEEE.

关键词： Artificial Intelligence (AI) Digital Twin Technology Electric Vehicle (EV) Battery Management Energy Efficiency Optimization Internet of Things (IoT) Predictive Maintenance

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer Systems science & engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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