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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies 1

Real-Time Safety Monitoring for Construction Sites Using RFI...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Thaleeparambil, Noeal Rajeev Biju, Abiya Krishna, Deva Prathap, Boppuru Rudra Rekha, V. Reddy, Vandana Department of Computer Science and Engineering Bengaluru India M. S. Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350355611

The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols. © 2025 IEEE.

关键词： Radio frequency identification (RFID)

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ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vagale, Ghanashyam Bhat, Shashidhar Y Padma Priya Dharishini, P. Gk, Prajwal Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350374711

This paper presents a novel framework that utilizes Google's Gemini Pro Vision large language model (LLM) and natural language processing (NLP) techniques to analyze and compare resumes or CVs with job descriptions. The framework not only evaluates the compatibility between the resume and job description but also provides personalized feedback and suggestions for improvement. By identifying potential gaps in skills and qualifications, and recommending relevant resources, the framework assists both human resources professionals in streamlining candidate shortlisting and empowers candidates to enhance their CVs. Gemini processes a resume as a Base64-encoded string and cleans up the job description using NLP. It then generates a personalized report with insights, including a compatibility score, skills analysis, improvement suggestions, and resource recommendations. Initial results demonstrate the framework's effectiveness in accurately assessing candidate suitability and providing valuable feedback. Future work will focus on expanding the framework to analyze multiple resumes simultaneously and developing more sophisticated algorithms for enhanced accuracy and reliability. This framework has the potential to revolutionize the recruitment process by streamlining candidate selection, empowering candidates to improve their CVs, and ultimately leading to better candidate job matches and a more efficient and equitable hiring landscape. © 2024 IEEE.

关键词： Visual languages

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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computer Vision assisted Deep Learning based Sitting Posture Suggestion System 3

Computer Vision assisted Deep Learning based Sitting Posture...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Vagale, Ghanashyam Prajwal, G.K. Kumar, Rohit Dharishini, P. Padma Priya Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user's posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user's daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm's efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals. © 2024 IEEE.

关键词： Cameras

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AI-Powered Mental Health Diagnosis: A Comprehensive Exploration of Machine and Deep Learning Techniques 2

AI-Powered Mental Health Diagnosis: A Comprehensive Explorat...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Supriya, M.S. Aniket, Antham Rakshitha, N.M. Aakanksha, J. Peter, Kenith Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382952

Individuals suffering from mental illnesses frequently communicate their sentiments and emotional states on social media through their posts. It is a challenge to recognize individuals suffering from mental health illnesses at an early stage and to provide them with necessary treatment. In this paper, an experimental methodology based on publicly available Reddit data to develop a mental health diagnosis system has been proposed. Natural language processing (NLP) techniques are exploited to help machine understand social media posts from Reddit, and deep learning and machine learning models are trained to classify the social media posts into one of the following four mental illnesses: depression, anxiety, schizophrenia and bipolar. A comparative study of these models' is performed and the results revealed that random forest outperformed other models, by obtaining 90% accuracy. In this study, we also design a web-based platform for deploying the model to aid in diagnosis of mental diseases because people suffering from anxiety or depression refrain from sharing their feelings with doctors, relatives, family, or friends. © 2024 IEEE.

关键词： Diseases

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Smart Traffic Management System for Efficient Mobility and Emergency Response

Smart Traffic Management System for Efficient Mobility and E...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sultana, Farhain Bhardwaj, Ritika Kumar, Rohan N. Gaur, Ruchir Shankar, Sahana.P. Bharadwaj, Aryan Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

This research addresses the critical issue of urban trafficcongestion exacerbated by the inadequacies of traditional traffic management systems. Inefficient adaptation to real-time traffic conditions by static traffic light timers leads to wasted time, and fuel, increased pollution, and compromised safety during peak hours and emergencies. To mitigate these challenges, this study proposes a novel Smart Traffic Management (STM) system that leverages cutting-edge technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The STM framework dynamically adjusts traffic flow in response to real-time data, optimizing efficiency and enhancing emergency response mechanisms. Through comprehensive simulations and case studies, this research demonstrates the potential of the STM system to alleviate urban congestion and improve overall traffic management in diverse urban settings. © 2024 IEEE.

关键词： Traffic congestion

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An Empirical Survey of Students' and Professors' Perspectives on computer science University Teaching 13

An Empirical Survey of Students' and Professors' Perspective...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Klopp, Marco University of Applied Sciences Aschaffenburg Faculty of Engineering and Computer Science Aschaffenburg Germany

ISBN: (纸本)9798350376234

This paper presents a study which examines the pedagogical-interactional dimensions in computer science education at universities of applied sciences, focusing on the expectations and perceptions of students and professors. Data were collected using a modified version of the Learning Culture Inventory (LCI) questionnaire and analyzed both descriptively and *** descriptive statistics reveal differences in the mean values between the ideal and real states of educational conditions as perceived by students and professors. Inferential statistical analyses using t-tests identified significant differences between the perceptions of the two groups. The effect size Cohen's d was also calculated to determine the strength of the existing difference. The analyses show that there are significant differences in many dimensions of the LCI between the perspectives of students and professors. These differences are often associated with high effect sizes. The results therefore indicate that the students' expectations and perceptions deviate (considerably) from the professors' perspectives. © 2024 IEEE.

关键词： computer science university teaching Learning culture Professors' perspectives Students' perspectives

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Towards end-to-end structure determination from x-ray diffraction data using deep learning

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npj Computational Materials 2024年第1期10卷 1048-1059页

作者： Gabe Guo Judah Goldfeder Ling Lan Aniv Ray Albert Hanming Yang Boyuan Chen Simon J.L.Billinge Hod Lipson Department of Computer Science Columbia UniversityNew YorkNY10027USA Department of Applied Physics and Applied Mathematics Columbia UniversityNew YorkNY10027USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC27708USA Department of Mechanical Engineering Columbia UniversityNew YorkNY10027USA

Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)*** many materials are readily available as crystalline powder,powder crystallography is of growing usefulness to many ***,powder crystallography does not have an analytically known solution,and therefore the structural inference typically involves a laborious process of iterative design,structural refinement,and domain knowledge of skilled experts.A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning,while capturing the ambiguities around molecule orientation,symmetries,and reconstruction *** we present an ML approach for structure determination from powder diffraction *** works by estimating the electron density in a unit cell using a variational coordinate-based deep neural *** demonstrate the approach on computed powder x-ray diffraction(PXRD),along with partial chemical composition information,as *** evaluated on theoretically simulated data for the cubic and trigonal crystal systems,the system achieves up to 93.4%average similarity(as measured by structural similarity index)with the ground truth on unseen materials,both with known and partially-known chemical composition information,showing great promise for successful structure solution even from degraded and incomplete input *** approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples,paving the way to reconstruction of yet unresolved nanostructures.

关键词： structure powder crystalline

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INTEROPERABILITY PLATFORMS: A COMPONENT BASED APPROACH TO DERIVE FUNCTIONAL CLASSIFICATION 9

INTEROPERABILITY PLATFORMS: A COMPONENT BASED APPROACH TO DE...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Staemmler, Martin Department of Electrical Engineering and Computer Science University of Applied Sciences Stralsund Germany

ISBN: (纸本)9789898704597

Interoperability platforms (IOPs) have been and are continuously designed, deployed and used for a variety of scopes, from simple data integration, reducing heterogeneity between data sources, data management systems without and with archival function up to allowing for process support and supporting data analysis. Furthermore, the scope depends on the intended use in acute care or research or both of them as well as on the stakeholders involved from health care professionals, external cooperating organizations and up to the involvement of patients. The objective of the paper is to derive a functional classification reflecting not only the IOP development over the last years but also anticipating upcoming designs. As such, four functional levels which build on top of each other have been identified: (i) management of unstructured data, (ii) management of structured data, (iii) process support, and (iv) data analysis. For each level, the respective system architecture is sketched, including functional components and references to relevant standards, profiles, and means for semantic annotation. Furthermore, the functional classification is applied to a set of projects and initiatives and allows assigning them to functional levels, thus enabling functional discrimination. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Interoperability

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