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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Laha, Suprava Ranjan Kumar Nayak, Debasish Swapnesh Utkal University Department of Computer Science & Application Odisha Bhubaneswar India Utkal University Department of Computer Science and Application Odisha Bhubaneswar India Siksha 'O' Anusandhan University Department of Computer Science & Engineering ITER Odisha Bhubaneswar India

ISBN: (纸本)9798350344882

Cloud Computing is increasingly being recognized as a valuable virtual resource-sharing solution. Due to its ability to provide solutions across different industries, the number of Cloud computing users has grown substantially. Load balancing distributes the workload across multiple servers to optimize resource utilization and prevent overload, while fault tolerance ensures that the system can continue to function even in the event of failures. In an e-healthcare system, where downtime or errors can have severe consequences for patients, fault tolerance and load balancing become even more critical. Load-balancing techniques are of utmost importance in enhancing the effectiveness of cloud servers. We have comprehensively examined the primary elements that impact fault tolerance in load-balancing task migration within cloud computing. These factors have been assessed by employing various methods to evaluate their influence on fault tolerance in load balancing. By implementing these techniques, e-healthcare systems can improve their ability to handle traffic surges and ensure patient safety and satisfaction. © 2023 IEEE.

关键词： Cloud computing

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Classification of Potato Leaf Disease using Deep Learning 3

Classification of Potato Leaf Disease using Deep Learning

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Singh, Neelam Sharma, Deeksha Dev, Saurav Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350354171

Deep learning is one of the subdomains of Artificial Intelligence. With the advantages of automatic learning and feature extraction, in recent years, it has become a hot topic in academic and industrial circles. Meanwhile, this has emerged as one of the hotspots within agricultural plant protection research, including plant disease recognition and pest range assessment. Deep learning in plant disease recognition can avoid the shortcomings of artificial selection of features of the disease spot, making the feature extraction of plant diseases more objective, promoting research efficiency, and increasing technological transformation speed. The goal of this work is to analyze potato leaf diseases using CNN in order to determine the model's optimal performance using a large dataset (10,000 photos) of potato leaves in the analysis. The most effective model will support farmers in making decisions that will keep potato production from declining. This investigation revealed that the model worked better when forty epochs were applied to the dataset of 10,000 photos. Additionally, three different epochs were applied and achieved 100% accuracy for 30 and 50 as achieved 99.97% and 99.98% respectively. By offering recommendations for how to categorise Potato Leaf Disease with the best result, the study made a substantial contribution to the agricultural industry and farmers. While working with big volumes of data, researchers should be concerned about the system because running code takes longer time. To focus this, a dataset including 2152 photos was gathered from Kaggle, with an additional 7848 images sourced from various platforms such as Google and raw data from Potato Field. To determine which epochs performed the best, a range of epochs were applied and results shows that 40 epochs led to 100% accuracy. © 2024 IEEE.

关键词： artificial intelligence deep learning potato disease

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An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

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computer Modeling in engineering & sciences 2023年第6期135卷 2349-2371页

作者： Mingyong Li Lirong Tang Longfei Ma Honggang Zhao Jinyu Hu Yan Wei College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331China

The learning status of learners directly affects the quality of *** with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile ***,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information ***,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,*** integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is *** to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple ***,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

关键词： Deep learning fatigue detection facial expression recognition sentiment analysis information fusion

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application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Structured Approach for Relation Extraction in Legal Documents 4

Structured Approach for Relation Extraction in Legal Documen...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Sasidharan, Anjali K. Rahulnath, R. Department of Computer Science and Application Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain's rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain. © 2023 IEEE.

关键词： Clustering methods Graph reasoning Knowledge graph creation Legal documents Machine learning Natural language processing Relation extraction

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An Innovative Machine Learning Approach for Breast Cancer Detection Based on Comparative Analysis 2nd

An Innovative Machine Learning Approach for Breast Cancer De...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Das, Manab Kumar Bhattacharjee, Swapnamoy Mondal, Saikat Goswami, Soham Department of Computer Application and Science IEM-UEM Kolkata India Department of CSE MCKV Institute of Engineering West Bengal Howrah India

ISBN: (纸本)9789819776023

The basic building blocks of the human body are cells, and cancer is defined as an abnormal proliferation of bodily cells. There are however numerous other causes of cancer. Cancer has emerged as one of the world's most perilous causes of death in recent years. The fatality rate of Breast Cancer (BC) is increasing globally on a daily basis. The majority of women worldwide lost their lives as a result of BC. In 2020, an estimated 2,261,419 new breast cancer cases for women were reported worldwide. Histopathology image data analysis can be used to diagnose or forecast breast cancer in the majority of instances, but this type of analysis at the genetic level is very time-consuming and expensive. In this study, the main objective is to examine various well-known Machine Learning (ML) techniques, including Logistic Regression, Random Forest, KNN, and Decision Tree, in order to investigate BC. This proposed technique introduces a hybrid approach based on the overlap value of particular features and also attempts to calculate the accuracy at every stage. Additionally, thoroughly examine the correctness of the suggested system in comparison to existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Solution to Web Scraping 11

Solution to Web Scraping

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11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Biswas, Chandan Mallick, Rahul Paul, Subrata Mukherjee, Dipta Master of Computer Application University of Engineering and Management Jaipur Jaipur India University of Engineering and Management Jaipur Dept of Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665475129

Data retrieval from a website, frequently automatically and without the owner's consent, is known as scraping. This information can also be utilized however the scraper sees fit. The action is considered criminal, but the legality has changed because hasn't stopped others from following suit. anti-scraping instruments are not used though. Anti-scraping Solutions are offered as fairly expensive services that are both slow, and effective services. This essay list offers suggestions for reducing impediments. the development of an anti-scraping System as a Product app for small- to medium-sized websites. are expensive. © 2023 IEEE.

关键词： Websites

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