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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Bharti, Jigyasa Dongre, Snehlata Thorat, Mayuri G H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India PES's Modern College of Engineering Dept. of Artificial Intelligence and Machine Learning Pune India

ISBN: (纸本)9798350383287

Analytics is a process of collecting and analyzing data that can help in taking meaningful decisions. The data is collected from various sources, this raw data is then processed and converted to dashboards which assists in business analyzation. Analytics are of various types such as descriptive, diagnostic, predictive, prescriptive and text. One area of analytics is Business intelligence which is a broader concept of using various tools and methods that involves presentation of business data to support decision making. Customer behavioral analysis can be conducted by having insights from web analytics. It is a process of understanding how a customer is responding to a website. Regular improvements can be done to increase customer interaction, enhance user experience and data-driven decision-making. In this paper, we will be discussing two major tools that are used for behavior analysis. © 2024 IEEE.

关键词： Decision making

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Enhancing Security in Automobile Edge Computing through Federated learning and Blockchain

Enhancing Security in Automobile Edge Computing through Fede...

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Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), International Conference on

作者： Sandosh S Sujay Doshi Atharva Joshi Faculty for Computer Science with specialization in Artificial Intelligence and Machine Learning Vellore Institute of Technology Chennai India Computer Science with specialization in Artificial Intelligence and Machine Learning Vellore Institute of Technology Chennai India

Vehicular networks that consist of interlinked automobiles and transportation network are prone to cyberattacks due to the increased use of software and the presence of wireless interfaces. To counter these threats, intrusion detection systems can be tailored efficiently. In recent years, there has been significant development in detection of malicious attack traffic through the use of machine learning. However, traditional systems require powerful computing devices to continuously train and update complex network models, which can reduce the efficiency and effectiveness of the system due to resource constraints, untimely updates and dealing with large volumes of data. To address this issue, a cooperative intrusion detection mechanism which had been proposed has been improved with a classification based novel approach, that eases the burden by distributing the training model to distributed edge devices such as connected vehicles and roadside units. The previous approach has a slow response time to the increasing network issues. The proposed work offers a new approach through federated learning with the Extra Trees Classification algorithm. The Extra Trees Classifier performs better than other machine learning models in all aspects. The proposed approach provides a faster response to security threats in automobile edge computing. The use of a distributed federated approach reduces the load on the server while maintaining confidentiality by aggregating models using a blockchain.

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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A Review on Implementation of Wearable Devices in Healthcare 7

A Review on Implementation of Wearable Devices in Healthcare

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Suman, Saurabh Choudhary, Vishal Kumar Pacharaney, Utkarsha Kumar, Abhishek Kumar, Dhiraj Raushan, Rahul Faculty of Engineering And Technology Department of Computer Science And Design Maharashtra Wardha India Faculty of Engineering And Technology Department of Computer Science And Medical Engineering Maharashtra Wardha India Faculty of Engineering And Technology Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350359299

The capacity to get additional patient data, including clinical, behavioral, and self-monitored data, has enhanced by the expanding usage of wearable technology. Large volumes of previously unobtainable data are now available, providing a chance to create new instruments with sophisticated analysis and decision support systems for application in clinical settings. Additionally, it gives patients additional options by arming them with personalized decision-support resources and information, enabling them to take charge of their own health care while maintaining their sense of autonomy. Especially well-suited for miniaturization and integration into microfluidic devices are biosensors with electrochemical sensors. Applications cover activities in the domains of diagnostics, food and environmental control, and the identification of complete cells, the parts of cells, proteins, and tiny compounds. Microfluidic technologies can be as basic as sample transport tubes or as sophisticated as platforms with extra components for sample preparation and sensing electrode integration. The kind of detection, application, and sample material all influence the microfluidics design. The next issue for the healthcare industry will be how to use newly acquired information in a way that benefits patients, healthcare organizations, and professionals all while reducing costs and workloads. Given the current limitations, the challenge for wearable technology designers is to provide solutions that healthcare practitioners can readily integrate and use. © 2024 IEEE.

关键词： Biosensors

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Automated Polyp Detection in Colorectal Cancer Diagnosis using Deep learning Techniques 8

Automated Polyp Detection in Colorectal Cancer Diagnosis usi...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik Vijay, K. Krissh Shankaran, N. Manohar, Eashaan Citibank Na Jersey CityNJ United States Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Colorectal cancer ranks second among female cancer diagnoses and third among male diagnoses. Most colorectal malignancies begin as intestinal polyps, accounting for at least 80%-95% of all cases. Given the increasing prevalence of colon cancer, better methods for detecting polyps in the colon early on are desperately needed. The prevention of colon cancer and the diagnosis of polyps in the colon are closely related. If polyps are removed during a colonoscopy, it is called a success. However, correctly identifying and categorizing polyps is not always easy. The goal of this research is to provide medical professionals who do colonoscopies with a tool that can quickly and accurately identify polyps in colonoscopy images by utilizing Deep learning (DL) and the image recognition capabilities of Convolutional Neural Network (CNN) models. Given the current system's many flaws, this is crucial. Although colonoscopies are highly operator dependent, which are still the diagnostic tool of choice for screening and diagnosis. The urgent requirement for reliable polyp detection in colonoscopy images drives most of this R&D. The research focuses on using deep learning to improve diagnostic accuracy in polyp diagnosis and deploying the implementation as an app that displays all the data image details upon upload. Information such as polyp size, growth detection, and polyp development stage. © 2024 IEEE.

关键词： Diseases

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Quantum Chain of Things: 'The Quantum triad - Integrating Blockchain, IoT and Quantum Computing for a Transcendent Future'

Quantum Chain of Things: 'The Quantum triad - Integrating Bl...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Jain, Shweta Aggarwal, Rishit Gandhi, Anju Bhandari Department of Artificial Intelligence and Machine Learning Panipat Institute of Engineering and Technology Panipat India Department of Computer Science and Engineering Graphic Era University Dehradun India

ISBN: (纸本)9781839539848

The Internet of Things (IoT), which is building a new world where everything is interconnected and can be remotely operated, is one of the developing technologies. The epoch of machines conquering the universe is just getting warmed up. The next technological innovation is blockchain, which uses a peer-to-peer communication link to preserve general transactional data and information (or blocks) across several repositories (or chains). The terminology "Digital Ledger" alludes to an instance of block storage. Quantum computing is the next quickly developing technology. It incorporates the use of numerous quantum physics aspects, notably superposition and entanglement. Every foundational premise of the computational game is amended by quantum excellence. A new paradigm dubbed Quantum - Chain of Things (QCoT), which is the most potent technology from the sub-emerging technologies is presented when we scrutinize the collaboration of the three powerful advanced. The BC-IOT (Blockchain coupled with IOT leveraging Arduino) and QC-IOT (Quantum computing merged with IOT using IBM Qiskit) subsystems are the cornerstones of QCoT technology. The prevailing breakthroughs in blockchain, quantum computing, and IOT will be addressed in this paper, followed by an exploration of the recommended architectural model for the improved technology. © The Institution of engineering & Technology 2023.

关键词： Blockchain

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Mobile-Based Fitness Monitoring System: A Smartphone-Centric Tracker without External Accessories 15

Mobile-Based Fitness Monitoring System: A Smartphone-Centric...

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

作者： Sujatha, E. Devi, P. Jayasri Archana Ilampiray, P. Kalaivani Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani India

ISBN: (纸本)9798331300579

This paper delves into the transformative impact of modern technology on human health, exploring the shift towards utilizing mobile applications as integral tools for health monitoring, control, and management. As society becomes increasingly interconnected and reliant on technology, concerns about the potential health implications arise. This paper investigates the nuanced relationship between technology and health, specifically focusing on the development of mobile applications as a paradigm shift from traditional external gadgets. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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A Review on the Applications of Natural Language Processing in Healthcare 4

A Review on the Applications of Natural Language Processing ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Warbhe, Mohan K. Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards. © 2024 IEEE.

关键词： Challenges of NLP in Healthcare Future treads of NLP for healthcare Healthcare NLP

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Feature Extraction and Similarity-Based Movie Recommendation Using Bag-of-Words Model

Feature Extraction and Similarity-Based Movie Recommendation...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Raj, Swati Dutta, Jhalak Bandopadhyay, Saikat Prasad, Rajesh Department of Computer Science and Engineering Christ University Bengaluru India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering MIT Art Design and Technology University Pune India

ISBN: (纸本)9789819613472

Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recommendations, offering a viable alternative to collaborative filtering methods. Unlike collaborative filtering, which relies on user ratings and demographic data, the proposed system operates without such prerequisites, making it particularly advantageous in scenarios with limited user engagement. By leveraging content attributes such as title, genre, cast, and crew, the system identifies similarities between movies and recommends those akin to a user’s preferences. Through innovative preprocessing techniques and feature extraction methodologies, the system transforms textual descriptions into numerical vectors, enabling efficient recommendation generation. The proposed model contributes to addressing critical challenges inherent in collaborative filtering, including the cold start problem, data sparsity, and popularity bias. Furthermore, it underscores the broad applicability of recommender systems across various domains and emphasizes the continuous evolution of interface designs and algorithms within the research community. Overall, the proposed content-based recommender system offers a promising solution for delivering personalized movie recommendations while advancing the state of the art in recommendation technology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bag-of-words Cosine similarity Filtering system Recommender system TF-IDF

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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