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Designing User-Friendly Interfaces with Human-Centered AI

Designing User-Friendly Interfaces with Human-Centered AI

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Hemalatha, Raavi Sujana, Korukonda Keerthi Kumar, Jagana Deepak Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798331543617

This research aims to explore the potential of using mortal-centric artificial intelligence in the process of creating interfaces that support the lifestyles of stoners. It emphasizes the importance of stoner experience and the practicality of the most advanced technologies now on the market. The goal of this research is to better understand the ideas behind mortal-centered design so that we can use AI to improve how stoners interact with digital interfaces. The goal of this investigation is to create user interfaces that can adapt to the unique requirements and tastes of each stoner. This is the inquiry's objective. To achieve this, the principles of intuitive design and adaptive literacy algorithms will be applied. To determine the effectiveness of these AI-powered interfaces, a mixed-style approach is required. This technique combines the qualitative input from stoners with the quantitative needs for usability. The idea of employing artificial intelligence focused on mortals to transform stoner interface design is being advanced, as the main findings show notable gains in stoner pleasure and engagement. The goal of this study is to contribute to the emerging field of human-computer commerce by providing insight into the creation of digital environments that are more user-friendly and accessible to stoners through the deployment of potent artificial intelligence techniques. By giving information regarding the creation of digital environments, this will be achieved. © 2024 IEEE.

关键词： User interfaces

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An Accurate Blood Pressure Prediction Based on Clinical and Physiological Data Using Machine Learning

An Accurate Blood Pressure Prediction Based on Clinical and ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Sivaji, U. Chatrapathy, K. Kiran, Ajmeera Shaker Reddy, Pundru Chandra Rao, P Varaprasada Partheeban, N. Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad India Reva University School of Computing & Information Technology Karnataka Bengaluru India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India Galgotias University School of Computing Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350308167

When attempting to diagnose hypertension or cardiovascular illness, blood pressure (BP) is an essential biological feature. Taking a reading is interrupted and can be painful when done with cuff-based equipment like a sphygmomanometer. Recently, a cuff-free approach has been presented that uses electrocardiogram (ECG) and photoplethysmography (PPG) signals. Due to the need for extensive multi-sensor collections, however, this approach is time-consuming and expensive. The current research uses a machine-learning(ML) strategy called classification-tree to examine the relationship between BMI, waist-circumference(WC), hip-circumference(HC), and waist-hip-ratio(WHR) in predicting hypertension(IBP). 400 college students, ranging in age from 16 to 63 (with women making up 56.3% of the sample), provided the data. In the training set, fifteen trees were generated for each sex by varying the number and order of predictors. Due to their low variance (89.56), low misclassification (.18), and high pseudo R2 (.43), BMI, WC, and WHR are the combination that yields the best anticipation for women. In the validation set, the model showed sensitivity of 82.54% and specificity of 83.68%, whereas in the test set, those numbers dropped to 45.65% and 65.15%, respectively. The lowest deviation (54.79), misclassification (.17), and highest pseudo R2 (.46) were found for BMI, WC, HC, and WHC when predicting male health. In the training set, this model showed sensitivity of 69% and specificity of 87.54%, whereas in the test set, these figures dropped to 58.38% and 69.70%, respectively. In the end, the classification-tree examination result was contrasted to the standard logistic-regression(LR) method, showing that the former was more accurate. © 2023 IEEE.

关键词： Blood Pressure Clinical data Hypertension Machine learning Physiological data prediction

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Deep Learning for MRI-Based Brain Tumour Identification and Classification 1

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8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Jareena Begum, D. Chokkalingam, S.P. Sundaravadivazhagan, B. Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Chennai Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences University of Technology and Applied Sciences Al Mussana Oman

ISBN: (数字)9789819732456

ISBN: (纸本)9789819732449

Radiology tumour spotting is complicated and requires medical knowledge. Thus, a lack of doctors should not delay cancer detection programmes. Biomedical image processing software helps find brain tumours in MRI data. This study segmented and detected brain tumours using MRI sequence images. This process is complicated by the similarity of normal tissues and the wide range of tumour tissues in different patients. Brain tumour detection is the main goal. Brain tumour diagnosis requires precise tumour size and position. This paper presents a deep learning-based brain tumour MRI segmentation and classification method. We preprocessed the photos with the Gaussian blur filter and image enhancement tool. Binary thresholding segments. Morphological methods reveal traits. CNN will assess brain MRI regularity. This study uses the Kaggle Dataset. We train with 255 brain MRIs, 155 with tumours and 98 normal, and achieve 97% accuracy. We test the model using Kaggle brain MR datasets. Brain tumour detection, segmentation, and categorization;medical image processing. This study proposed an easy-to-implement way for identifying and classifying bone tumours. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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EVGPT - Fine-Tuned Large Language Model for Electric Vehicle Operational Assistance 4

EVGPT - Fine-Tuned Large Language Model for Electric Vehicle...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Kanimozhi, S. Prithvi, G. Barath, M. Vaikash, K.G. Mathew, Abel Manoj Gukhan, V.B.S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331528614

The optimization of electric vehicle (EV) utilization and efficiency is becoming increasingly essential in the ever-changing landscape of sustainable transportation. Consulting user manuals, online resources, and seeking expert advice for EV maintenance and operation is a significant obstacle to productivity. Electric Vehicle GPT (EVGPT) is a specialized Large Language Model (LLM) specifically designed to address queries related to EV maintenance, operation, and troubleshooting, filling the gaps that conventional LLMs may miss. This model serves as a comprehensive digital assistant for technicians, operators, and EV enthusiasts, providing timely and precise information while upholding response quality. This study offers an overview of the EVGPT model architecture and presents an operational evaluation based on its implementation in a specific EV model. ©2024 IEEE.

关键词： Electric vehicles

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Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning 40

Clients Help Clients: Alternating Collaboration for Semi-Sup...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Jiang, Zhida Xu, Yang Xu, Hongli Wang, Zhiyuan Qiao, Chunming School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China University at Buffalo Department of Computer Science and Engineering The State University of New York United States

ISBN: (纸本)9798350317152

Federated learning (FL) provides a distributed framework for multiple clients to collaboratively train models without exposing raw data. Most FL research assumes that all clients have fully labeled data, which is impractical for many real-world applications. To this end, we focus on semi-supervised FL (SSFL), where data samples of each client are partially labeled. However, existing SSFL methods ignore two inherent characteristics of FL: limited communication resources and heterogeneous data distribution, which severely hinder convergence stability and efficiency. This paper proposes a novel SSFL mechanism, called FedAC, to address the above two challenges by alternating client-to-client (C2C) collaboration. Specifically, we group all clients using different clustering strategies at two different training stages. During each global round, FedAC first performs similarity clustering based on local data distribution, which gathers the knowledge from similar clients to generate high-quality pseudo-labels for unlabeled data. Then the clients are re-grouped using dissimilarity clustering strategy to approximate the IID setting at the cluster level, thereby alleviating the bias induced by Non-IID data. FedAC adopts a reinforcement learning algorithm to achieve a balance between labeling assistance from similar clients and unbiased optimization from dissimilar clients. Extensive evaluations demonstrate that FedAC can improve model accuracy and save up to 59.65% of communication costs compared with existing benchmarks. © 2024 IEEE.

关键词： Efficiency

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A Novel Deep Learning Approach Toward Efficient and Accurate Recommendation Using Improved Alternating Least Squares in Social Media

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 657-675页

作者： Dhawan, Sanjeev Singh, Kulvinder Batra, Amit Choi, Anthony Choi, Ethan Department of Computer Science and Engineering University Institute of Engineering and Technology Kurukshetra University Haryana India Department of Electrical and Computer Engineering Mercer University MaconGA United States

The increasing number of electronic transactions on the Internet has given rise to the design of recommendation systems. The main objective of these systems is to give recommendations to the users about the items (i.e., movies, music, books, etc.) they are likely to purchase in near future. These recommendations are on the foundation of the response provided by the other individuals about these products in the terms of ratings, which are in the form of a five-star rating system. Plenty of algorithms have been proposed in the past for building recommender systems. The most popular of these algorithms comprise content-centered and collaborative filtering recommendation methodologies. Recently, matrix factorization has appeared as a technique in recommender systems. Several algorithms centered on matrix factorization have been suggested in the recent past. In this paper, an improved alternating least squares algorithm has been proposed for building more efficient and accurate recommender systems which improves the performance of matrix factorization. The performance of the model has been evaluated using metrics like root-mean-square-error and execution time. However, in general, the time complexity of both algorithms is O(n^3), where n is the number of users or items. This is because both IALS and ALS involve iteratively solving a system of linear equations, and the time complexity of solving a system of linear equations is O(n^3). However, IALS can achieve significant performance improvements over ALS, particularly for large datasets, due to its incorporation of stochastic gradient descent and parallelization techniques. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Detection of Fake Reviews in E-Commerce Platforms Related to Ayurvedic Cosmetics in Sri Lanka 6

Detection of Fake Reviews in E-Commerce Platforms Related to...

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6th International Conference on Advancements in computing, ICAC 2024

作者： Fernando, Shehani Tissera, Muditha University of Kelaniya Department of Statistics & Computer Science Sri Lanka University of Kelaniya Faculty of Computing and Technology Sri Lanka

ISBN: (纸本)9798331517878

The Ayurvedic cosmetics industry in Sri Lanka is grappling with the prevalence of fake reviews. To mitigate this problematic situation, this study first utilized Natural Language Processing (NLP) techniques to analyze English-language un-structured text reviews from popular e-commerce platforms. Then, it incorporated three primary approaches: a rule-based algorithm, a machine learning model using logistic regression, and a deep learning model based on Long Short-Term Memory (LSTM) to identify fake news. Initially, rules for the rule-based al-gorithm were crafted with guidance from existing literature. This research investigated various feature engineering techniques and model architectures to enhance prediction accuracy and assessed the model performance through precision, recall, and F1 score metrics. The rule-based approach yielded a 50% accuracy, which was sub-optimal. However, the Logistic Regression model and the LSTM model obtained 89% and 80% accuracies respectively, out of which the LR model shows the highest accuracy. These findings demonstrate the effectiveness of AI-based technologies in detecting fake reviews, thereby fostering a more transparent and ethical market for Ayurvedic cosmetics in Sri Lanka. © 2024 IEEE.

关键词： Cosmetics

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An Improved Fuzzy Grounded Dynamic Package Priority Resolve and Line Management System in Wireless Sensor Network

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SN computer science 2025年第2期6卷 1-12页

作者： Songare, Lokendra Singh Kumhar, Bhupchand Rajput, Ajeet Singh Shukla, Prashant Kumar Nigam, Ankita Gautam, Prakhar Ahluwalia, Ritu Department of Computer Science and Engineering Medicaps University MP Indore India Department of Computer Science & Engineering IES Institute of Technology and Management IES University MP Bhopal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deamed to be University Vaddeswaram Andhra Pradesh Guntur 522302 India Princeton Institute of Engineering and Technology for Women Telangana Hyderabad India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSBS Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line Management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DMP FDMP PPI QOS WSN

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An Approach for Early Detection of Schizophrenia 15

An Approach for Early Detection of Schizophrenia

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Paranagama, Sachin Uditha, Isiwara Daundasekara, Pamodya Hettiarachchi, Shehani Weerasinghe, Lokesha Kasthurirathna, Dharshana Sri Lanka Institue Of Information Technology Faculty Of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350370249

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Mobility with Next-Generation Partial Hand Prosthesis Using Arduino Uno and Flex Sensors 2

Enhancing Mobility with Next-Generation Partial Hand Prosthe...

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2nd International Conference on Cyber Physical Systems, Power Electronics and Electric Vehicles, ICPEEV 2024

作者： Matury, Sai Sathwik Sam Stewart, X. Ra, Nawin Kumba, Krishna School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350388664

The proposed work delves into the comprehensive design, development, and evaluation process of a 3D printed prosthetic arm, employing servo motors controlled by an Arduino Uno microcontroller to enable gesture mimicry through flex sensors integrated into a wearable glove. The primary objective is to provide an affordable and accessible solution for individuals with upper limb disabilities. The paper outlines the intricacies of the design process, system architecture, programming nuances, and rigorous testing procedures, showcasing promising results in terms of gesture accuracy, adaptability, and user-friendliness. The prosthetic arm demonstrates high precision in replicating various hand gestures. Future work will focus on enhancing the durability and responsiveness of the system to further improve its practical application. User feedback has been overwhelmingly positive, indicating significant potential for real-world adoption. © 2024 IEEE.

关键词： Servomotors

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