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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu School of Computer Science and Technology Huaibei Normal University School of Computer Science and Information Engineering Changzhou Institute of Technology School of Computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： software quality Data security train ticket purchase system component-based software software trustworthiness trustworthiness measurement model software quality evaluation Object-oriented programming object-oriented programming Software engineering techniques trusted computing user feedback

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Revolutionizing Smart Agriculture: Burning Number Analysis and Linear Regression for Optimal Sensor Activation in 3-Ary n-Cube Networks

Revolutionizing Smart Agriculture: Burning Number Analysis a...

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2024 International Conference on Integrated Circuits, Communication, and Computing systems, ICIC3S 2024

作者： Rajeshwari, S. Rajesh, M. School of Advanced Sciences Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350364088

This study introduces a novel approach by combining burning number analysis and supervised machine learning, specifically linear regression, to optimize sensor placement in large agricultural fields modeled as a 3-ary n-cube network. In this unique integration, each node represents a location equipped with environmental sensors, and edges signify wireless communication links. The burning number analysis identifies critical sensor nodes, strategically activating them for efficient information propagation throughout the network. Simultaneously, linear regression is employed to predict and optimize the activation of key sensors, maximizing coverage and data dissemination. This approach aims to enhance network efficiency, providing valuable insights for precision agriculture practices such as crop management, irrigation scheduling, and resource allocation. Through this innovative method, the study not only contributes to the development of adaptive solutions for sustainable farming but also showcases the potential of combining graph theory-based burning number analysis with linear regression for optimizing sensor networks in complex agricultural landscapes. © 2024 IEEE.

关键词： Crops

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Hand-Gesture Based Emoji Identification and Chat Integration 5

Hand-Gesture Based Emoji Identification and Chat Integration

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tare, Sanskar S Patil, Madhav J Alam, Adnan Swarnalatha, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350321425

Every aspect of human life is slowly getting computerized and gradually turning electronic. This has made a rangeof tasks a lot easier and accessible for people from all walks of life. Unfortunately, this rapid computerization and the ever changing landscape of electronics has given birth to a new classof computer - illiterate individuals. Such individuals exist due to the lack of accessibility to latest electronic gadgets, or humansocial problems like old age. There is a need to make technology fun and easier to use for individuals belonging to such classes. The very primary use of internet-based networking is text-based communications namely: e-mails, texts chats, blogs, etc. The text based interaction is mainly facilitated using a keyboard which makes it the most focal human-computer interaction involved. The modern usage of this text-based communication involves extensive usage of emoticons. While most handheld devices feature a customised emoji-keyboard, the emoji keyboard is difficult to access in personal computers. This can adversely affect the interactions the aforementioned computer-illiterates partake in. This project aims to deal with this problem by creating a hand-detection based emoji detection algorithm in python through OpenCV and Mediapipe for detection of hand landmarks and implementing it in one such communication channel. The algorithm consists of two modules: a Hand-Tracking Moduleand an emoji detector capable of matching the tracked hand to existing emoji database. The resulting application is consistent, allow frequent users to use shortcuts and make the user feel more in control. The work also strives to integrate error prevention and simple error handling in existing emoji detection technologies along with managing short term memory load. © 2023 IEEE.

关键词： Palmprint recognition

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Advancing Virtual Reality Interaction through Real-Time Hand Gesture Recognition for Immersive Experiences 3

Advancing Virtual Reality Interaction through Real-Time Hand...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Singh, Vinayak Kumar Kalinathan, Lekshmi Marimuthu, Marimuthu Vadivel, Sengazhani Murugesan Palani, Saravanan Vellore Institute of Technology School of Computer Science and Engineering Chennai India VIT Business School Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350372120

Virtual Reality technology offers immersive experiences, but reliance on handheld controllers can limit immersion. This study explores real-time hand gesture recognition to enhance natural interaction in VR environments. Motivated by VR's diverse applications in gaming, education, and training, it seeks to improve engagement and realism. The study provides an overview of current hand gesture recognition techniques, including computer vision and deep learning methods. Understanding the state of real-time gesture recognition informs future research and development in VR interaction. It enables researchers, developers, and practitioners to make informed decisions about integrating and enhancing gesture recognition technologies in various VR applications © 2024 IEEE.

关键词： Virtual reality

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on computer Vision Techniques

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computer Modeling in engineering & sciences 2024年第4期139卷 759-782页

作者： Raveena Selvanarayanan Surendran Rajendran Youseef Alotaibi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

关键词： computer vision coffee berry disease colletotrichum kahawae XG boost shapley additive explanations

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Lung Cancer Classification and Localization Model using DTCWT and ANN-BPN 5

Lung Cancer Classification and Localization Model using DTCW...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Siva Priya, M.S. Manivannan, KiranKumar Vivekanandan, M. Saranya, G. School of Computer Science and Engineering Vellore Institute of Technology Chennai campus India School of Electronics Engineering Vellore Institute of Technology Chennai campus India

ISBN: (纸本)9798350379945

Early and accurate lung cancer detection is crucial for improved patient outcomes. This research proposes a hybrid framework combining traditional image processing techniques and machine learning for enhanced lung cancer diagnosis. By leveraging CT images, the system employs DTCWT and GLCM for feature extraction, followed by ANN-BPN for classification. Fuzzy clustering is utilized for malignant tissue localization. The proposed approach demonstrates superior performance in terms of accuracy, precision, and F1-score compared to conventional methods. This hybrid model offers a promising tool for assisting radiologists in lung cancer diagnosis. © 2024 IEEE.

关键词： Lung cancer

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