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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Meenakshi Sundaram, A. Joyce Beryl Princess, P. Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

In the age of e-commerce, it is critical for companies to comprehend the thoughts and feelings of their customers in order to enhance their goods and services. A powerful method for deriving insights from consumer feedback is the combination of aspect sentiment analysis and triplet extraction. The objective of this method is to recognize the characteristics or facets of a good or service under discussion, gather feedback on each component, and create connections between the components, feedback, and viewpoints. Businesses can gain important insights into customer feelings towards certain features like design, usability, price, and customer service by examining customer reviews, comments, and ratings. Opinion sources (experts, customers, etc.), sentiment expressions (positive, negative, or neutral), and opinion targets (aspects) are all identified from the text as part of the triplet extraction process. This gives companies a comprehensive grasp of the thoughts and preferences of their customers, which helps them design products, set prices, and provide customer service with more focus and eventually increase customer happiness. © 2024 IEEE.

关键词： Sentiment analysis

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Zenvgg1X: An Advance Hybrid Framework based on Gene Sequencing for Analysis and Prediction of Lung Cancers using KNN-GNN and Custom VGG19

Zenvgg1X: An Advance Hybrid Framework based on Gene Sequenci...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pandit, Kalpa Chatterjee, Ahan Chowdhury, Debtan Chatterjee, Debraj Techno International New Town Dept of Artificial Intelligence and Machine Learning Kolkata India Techno International New Town Dept of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350390759

Lung cancer affects about 238,340 people annually, of which men account for 117,550 cases whereas women have 120,790. Lung cancer causes roughly 127,070 deaths every year. The survival rate for lung cancer is only 56 percent if it can be detected within the lungs;however, it drops to just 5 percentage where there are distant tumors. As few as 16 percentage of cases can be diagnosed if the cancer is identified early. The study created a new lung cancer detection framework, Zenvgg1X a diagnosis and predictive framework combining VGG19, which is a deep convolutional neural network (CNN), and K-Nearest Neighbors - Graph Neural Network (KNN-GNN) hybrid. The combination of these two allows analyzing both images and texts, thus increasing the success rate of lung cancer detection. By working with medical images for identification purposes only the VGG19 model that is well recognized in terms of image classification analysis for lung cancer while KNN-GNN determine patterns from text related to diseases-which if you ask me, the combinations sometimes provide statistical outputs that are mindbogglingly high in accuracy such as 87.5 percent using only 30 percent of the data or more impressively reaching up to an astonishing figure around 98 percent with larger dataset. Further inclusion of Tp53 gene mutation data can enable early-stage prediction of lung cancer which may lead to improved survival rates. It goes without saying that this hybrid model represents a giant stride in medical diagnostics because it offers hope for what could eventually become a simple routine test for early diagnosis and treatment of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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Smart Security Management using IoT and HC-05 Bluetooth Module 8

Smart Security Management using IoT and HC-05 Bluetooth Modu...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Sam Michael, S. Ancy Jenifer, J. Benjamin, Ruth Moly Jacob, Cyril Shanthosh, S. Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350386578

Safety and security are essential for every individual worldwide, particularly in the context of home security. Modern advancements in science and technology have introduced various innovative solutions, including smart security locking mechanisms. This research study presents a novel smart lock system that leverages mobile phone applications integrated with built-in fingerprint authentication. Unlike other smart locks that rely on external biometric devices, this system utilizes the smartphone's internal fingerprint sensor, thereby reducing hardware costs and complexity. Additionally, the application supports multiple authentication methods, such as fingerprint and facial recognition, enhancing security and convenience. This solution eliminates the risk of key loss and ensures that only authenticated users can access the home. The lock mechanism operates automatically, allowing the door to open and close without human intervention, providing a seamless and secure user experience. © 2024 IEEE.

关键词： Keys (for locks)

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A Review of Vehicular Adhoc Network Routing and Congestion Control

A Review of Vehicular Adhoc Network Routing and Congestion C...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Kori, Kaveri Sridevi, H. Sharnbasva University Department of Computer Science and Engineering Karnataka Kalaburagi585103 India Sharnbasva University Department of Artificial Intelligence and Machine Learning Karnataka Kalaburagi585103 India

ISBN: (数字)9798350363289

ISBN: (纸本)9798350363289

In VANET, the efficiency of the RSUs positioned beside the roadways is crucial for both transportation and service provision. Direct communication between vehicles and infrastructure is supported by the network (V2V and V2I, correspondingly). The architecture of a VANET should satisfy several requirements to function properly and enable dependable real-time communication between vehicles while considering their low-power radio transmission of units, dynamic topology, and high mobility. Numerous routing protocols are suggested and assessed for VANETs. There are very few testbeds worldwide since they need a large count of wireless devices to test routing protocols with genuine VANETs and MANETs prototypes. By examining 25 publications, this survey aims to create a brief summary of VANET routing. In the current work, it offers an analysis of reliable routing models based on optimization algorithms. The best outcomes will be displayed once the performance metrics of the works that were examined are examined. © 2024 IEEE.

关键词： Congestion control (communication)

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Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetable Recognition and Calorie Measurement using Image Processing and Convolutional Neural Networks 5

Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetab...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Leo, Sam S. Benjamin, Ruth Moly Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350361155

The "Enhancing Nutrition Tracking" wander epitomizes a groundbreaking agreeable vitality between fake bits of knowledge (AI) and food science. Through the utilization of Convenient Net convolutional neural frameworks, this creative system passes on real-time normal item affirmation and correct calorie estimations, in this way empowering clients to make taught dietary choices. Central to its value is the visual emphasis on carbohydrate substance, ensuring clients have a comprehensive understanding of their food choices and dietary intake. This project's trademark lies in its user-friendly interface, arranged to streamline the strategy of capturing characteristic item and vegetable pictures for incite dietary bits of information. By reliably joining AI advancement into conventional assignments, individuals can effortlessly get to basic wholesome information, empowering more advantageous eating affinities and way of life choices. Besides, continuous ask almost endeavors search for to refine the system's real-time characteristic item recognizable confirmation capabilities and examine the immersive potential of virtual reality for in-depth wholesome assessments. By initiating the integration of AI and virtual reality advancements, the "Enhancing Nutrition Tracking" amplify focuses to catalyze a worldview move in food watching. © 2024 IEEE.

关键词： Fruits

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AI-Driven Diagnostics: Comparative Analysis of machine learning and Deep learning Approaches in Multiple Sclerosis Diagnostics 11

AI-Driven Diagnostics: Comparative Analysis of Machine Learn...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Anitha, L. Narmadha, D. Naveen Sundar, G. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Multiple Sclerosis (MS) is a chronic autoimmune disorder that targets the central nervous system, resulting in considerable physical and cognitive disabilities. Timely and precise diagnosis is essential for the effective management and treatment of Multiple Sclerosis (MS). Predicting MS involves using various types of data to assess the risk and diagnose the disease. The data commonly used are clinical, imaging, speech and gene data. This work presents a review of machine learning and deep learning methods, encompassing a broad spectrum of algorithms such as Support Vector machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and artificial Neural Networks (ANNs). Furthermore, the survey gives a comparative analysis of the dataset used, algorithms employed and the performance metrics. The survey also compares the implemented results with DL using the gene data. It also identifies current challenges in MS prediction, such as heterogeneity of MS, integration of multimodal data, validation and clinical implementation. Our goal is to offer a valuable resource for researchers and practitioners. © 2024 IEEE.

关键词： Convolutional neural networks

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Develop a Smart Android Application to Provide Laundry Service 15

Develop a Smart Android Application to Provide Laundry Servi...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jeyapradesh, Samuel Jebaseeli, T. Jemima Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798331300579

In the era of the Internet and smart applications, companies and institutions providing various services face significant challenges. The digital landscape, characterized by limitless connectivity and accessibility, poses a new paradigm where services transcend traditional boundaries of time and space. To remain at the forefront in their respective domains, businesses must embrace the electronic realm. Notably, web programming languages like PHP and ASP, alongside mobile application development languages such as Android have emerged as pivotal tools in this technological evolution. This design focuses on the development of an Android application integrated with a web control panel tailored for the service sector, with a specific emphasis on public laundry facilities. The study encompasses the system's architecture technological frameworks employed, and subsequently delves into the target audience, market requirements, and additional services to be offered. Throughout this exploration, the design aims to harness the potential inherent in these programming languages, drawing parallels to human language for enhanced user engagement and accessibility. © Grenze Scientific Society, 2024.

关键词： Laundries

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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Brain Tumor Detection using YOLO v7 and YOLO v8

Brain Tumor Detection using YOLO v7 and YOLO v8

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Vaghela, Rohan Sarda, Jigar Thakkar, Amit Vaishnani, Darsh Khokhariya, Avani Mehta, Pooja Charotar University of Science & Technology Department of Computer Science & Engineering Anand India Charotar University of Science & Technology Department of Artificial Intelligence & Machine Learning Anand India

ISBN: (纸本)9798331533311

The architecture of the brain is incredibly complex, with multiple areas managing various parts of the nervous system. An adult's health is greatly at risk from brain tumors because of their ability to severely impair organ function or possibly cause death due to their rapid growth of abnormal brain cells. Brain tumors can develop in any area of the skull or brain, including the protective layer around the brain and many other areas. Since these tumors can have a wide range of shapes, sizes, and locations, magnetic resonance imaging (MRI) is an essential tool for locating malignant tumors. MRIs are examined manually in order to diagnose brain tumors. However, manually identifying brain tumors is a challenging and time-consuming task that may result in errors. As a result, computer-aided brain tumor detection has been considered a crucial area of study in biomedical engineering. However, because tumor sizes and shapes vary enormously throughout subjects, developing a reliable brain tumor detector remains difficult. Thus, for the purpose of accurately identifying brain tumors in MRI scans, two popular deep learning detection algorithms - YOLO v7 and YOLO v8 - were tested and compared in this study. From the results, YOLO v8 model has achieved highest validation accuracy of 96.50% with average precision and recall value of 95.9% and 93.3% respectively, while YOLO v7 has validation accuracy of 87.6% with average precision and recall value of 81.9% and 85.4% respectively. © 2024 IEEE.

关键词： Deep learning

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