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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach

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Journal of Mobile Multimedia 2023年第2期19卷 363-388页

作者： Kumar, Sunil Kumar, Dilip Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur India

Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence. © 2022 River Publishers.

关键词： AES asymmetric encryption authentication blowfish cloud storage security ECC Symmetric encryption

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Solar Energy based Smart Home Monitoring and Automation using IOT 4

Solar Energy based Smart Home Monitoring and Automation usin...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dharmaraj, N. Adithyaa, A. Satyanarayanaa, H. Mouleesh, Y. Rupesh, S. Priyadharshini, K. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Rajalakshmi Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

In the pursuit of sustainable living and energy efficiency, integrating renewable energy sources with smart home technologies has become increasingly important. This paper presents a novel system that utilizes solar energy as the primary power source for a smart home, combined with Internet of Things (IoT) technology for monitoring and automation. The primary goal of this project is to reduce waste in conventional ways by making extensive use of energy recovery sources, enhancing safety, and removing environmental impact. Optimizing the equipment's use of program energy in accordance with the actual time of solar power generation is one of the key goals. Another is enhancing the effectiveness of energy transfer through remote monitoring and control using data and smartphone applications. The integration of Internet of Things (IOT) technology with solar energy systems is critical to the sustainability, efficiency, and administration of residential surroundings. This article investigates the deployment capabilities of the Internet of Things (IOT) for solar household energy system automation and monitoring. Future house automation will rely on solar energy and Internet of Things monitoring technologies once this work is completed. © 2024 IEEE.

关键词： Smart homes

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A Futuristic Optical and Wireless Convergence Network Towards 6G and Beyond 4

A Futuristic Optical and Wireless Convergence Network Toward...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Molefe, Mlungisi Sibiya, Khulekani Nleya, Bakhe Durban University of Technology Dept. of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Emerging use cases and stringent requirements for 6G and beyond networks require new quantum leaps in mobile communication due to the demands of higher throughput, extensive bandwidth requirements, ultra-low latency and massive number of connected devices. The advancement in wireless communications and the advancement in optical networks will be essential in addressing the demands for future mobile networks by providing an innovative infrastructure for 6G and beyond networks. To support stringent requirements for 6G networks and beyond, new innovative technologies and systems are required that will combine optical and wireless networks. An innovative approach of converging optical and wireless networks promises to improve the network into new heights by creating a smart world with beyond-human cognitive communication capacity and speed, improved network scalability and enhanced energy efficiency. Research has shown that a promising architecture to achieve this goal, and cater for extremely high bandwidth and extremely low latency requirements is based on an All-Photonics Network. Therefore, in this paper, we propose an All-Photonics Network for the future to address bandwidth limitations, latency issues and massive connectivity. Furthermore, we propose an evolved infrastructure or architecture converging optical and wireless networks. Potential benefits for the evolved infrastructure are also discussed. © 2024 IEEE.

关键词： Bandwidth

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Machine Learning Approach for Career Guidance Using Brain mapping Technique and Psychometric Test 1

Machine Learning Approach for Career Guidance Using Brain ma...

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

作者： Suryawanshi, Soham Kolhe, Chaitanya More, Pranav Barhate, Jayesh Deshmukh, Rushali Hingmire, Amruta JSPM's Rajarshi Shahu College of Engineering Dept. of Computer Engineering Pune India School of Computer Engineering and Technology PCET's Pimpri Chinchwad University Pune India

ISBN: (纸本)9798350355611

After completing upper secondary education, students all over the world commonly struggle to make the crucial decision of pursuing a decent job path. Many of them lack the maturity and experience required to make sound judgments at the age of 18, leaving them concerned about their future. The challenge of identifying whether they have the necessary skills for their chosen vocation adds to the difficulty of the task. Our answer to this problem is an automated career counselling system based on psychometric tests and brain mapping techniques employing scenario-based testing as an approach using machine learning ideas that supports students in picking the optimal career path according to their interests and skill set. The user will first take the Psychometric test, which will help us analyse the user's intelligence ability, followed by another test using the brain mapping technique to help uncover his personality qualities. Based on these two tests, the ultimate outcome will be evaluated by balancing the results of the psychometric and brain mapping tests and forecasting a viable and relevant career choice, which will be backed by college admission support, a thorough career plan, and opportunities for additional study. © 2025 IEEE.

关键词： Brain mapping

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Bare Printed Circuit Board Defects Localization and Detection Using YOLOv5 Models 6

Bare Printed Circuit Board Defects Localization and Detectio...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Khan, Wajih Ahmed Hamza, Amir Akram, Muhammad Usman Khan, Umar Shahbaz Nawaz, Tahir National University of Sciences and Technology Dept. of Mechatronics Engineering Islamabad Pakistan National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

A printed circuit board (PCB) is a vital component of any electronic device. Over the years, the need to manufacture PCBs in large volumes has become a necessity due to technological advancement and the expansion of the consumer market. Small defects in the PCB signal trace can have adverse consequences on the functionality of an electronic device. Initially, the quality control process of PCB inspection was carried out manually which was not only tedious but also susceptible to human error. To automate this process, different techniques were developed using classical machine vision that comprised reference comparison, subtraction of images and morphological operations. The methods were effective and reduced inspection time but were sensitive to noise, rotation, and scaling of images. It was difficult to detect defects in complex patterns and only a small fraction of defects could be detected through classical techniques. In this study, four YOLOv5 models were tested for the classification and localization of defects in PCBs. Transfer learning on the bare PCB defect dataset was implemented using pretrained weights of YOLOv5. The PCB dataset was composed of 2,079 images with 6 classes of PCB defects. Data augmentations were carried out to increase the dataset which enhanced performance and generalizability of the models. The models were trained and tested at different image resolutions. Promising experimental results were achieved with the medium YOLOv5 model reaching a mean average precision of 96.92%. © 2024 IEEE.

关键词： Machine vision

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Gaze-Assisted Autism Spectrum Disorder Identification: A Fusion of Machine Learning and Deep Learning Approaches for Preemptive Identification 5

Gaze-Assisted Autism Spectrum Disorder Identification: A Fus...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Simon, Judy Kapileswar, N. Datchinamoorthi, M. Muthukumar, S. Keerthana Devi, G. SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India SRM Institute of Science and Technology Dept of Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350395235

This research proposes an all-encompassing method for diagnosing Autism Spectrum Disorder (ASD) by leveraging eye-tracking data and advanced machine learning techniques. In this study, gaze-tracking data was collected from individuals diagnosed with Autism Spectrum Disorder and those not exhibiting ASD characteristics as they participated in various tasks and stimuli. Principal Component Analysis was employed to streamline the eye-tracking data, retaining crucial information while enhancing computational efficiency. These refined features were then integrated into Convolutional Neural Networks as the primary modelling approach. CNNs excel in recognizing relevant spatial and temporal patterns within the visual data, leading to intricate feature extraction and classification. Additionally, we explored the efficacy of transfer learning by enhancing pre-trained CNN models with our eye-tracking dataset. This approach enables us to leverage knowledge from large-scale visual tasks, potentially enhancing the model's performance in ASD classification. The research findings highlight the success of this multifaceted approach in accurately categorizing individuals with ASD. The synergy of ML, CNN, and transfer learning models significantly enhances the accuracy of ASD diagnosis, offering a promising avenue for early identification and intervention. Consequently, this research has the potential to aid in the creation of accurate noninvasive instruments for diagnosing ASD, thereby accelerating timely assistance and treatment for individuals impacted by the condition. © 2024 IEEE.

关键词： Image classification

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Watch Me: A CNN Based Platform for Bird Species Identification and Educational Resources 8

Watch Me: A CNN Based Platform for Bird Species Identificati...

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

作者： Gaur, Nidhi Kodankandath, Jenica Mhasal, Tanvi Mukhtiar, Jasmitkaur Nadar, Rexlin St. Francis Institute of Technology Computer Engineering Dept Maharashtra Mumbai India

ISBN: (纸本)9798350376425

Bird recognition is a fundamental task in ornithology, ecology, and conservation biology, aiding in population monitoring, habitat assessment, and biodiversity conservation efforts. Traditional methods of bird identification often rely on manual observation, which can be time-consuming, subjective, and error-prone, particularly when dealing with large datasets or visually similar species. In recent years, advances in deep learning, particularly Convolutional Neural Networks (CNNs), have revolutionized the field of image recognition, offering promising avenues for automating bird species identification. Evaluation of the proposed CNN-based bird recognition system involves comprehensive experimentation and validation on separate test datasets. Performance metrics such as accuracy, precision, recall, and F1 score are computed to assess the model's effectiveness in distinguishing between different bird species. Comparative analysis with existing methods and benchmark datasets provides insights into the strengths and limitations of the proposed approach. This research contributes to the ongoing efforts in utilizing machine learning techniques for advancing bird recognition capabilities, with implications for biodiversity conservation, ecological research, and environmental monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Physical Layer Impairments Resources Allocation Aware Algorithm/Framework in Transparent OTNs 4

Analysis of Physical Layer Impairments Resources Allocation ...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Nleya, Bakhe Molefe, M. Khulekani, Sibiya Durban University of Technology Dept. of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Present-day, limitedly fixed wavelength routed based all Transparent Optical Transport Networks (OTNs) are quite energy efficient in comparison to translucent and opaque equivalents. As a result they are also regarded as a practical solution transport network provisioning ultrahigh bandwidth capabilities to support the various applications and services. The resulting heterogeneous nature of present-day global traffic makes such wavelength-routed networks spectrally inefficient. However, advancements in DSP techniques, modulation approaches, and transmission impairment mitigation techniques have vastly improved such net-works' spectral efficiencies and optical reach. The paper thus reviews the efficiency in the operation of an orthogonal frequency division multiplexing (OFDM) based Transparent OTN that is characterized by flexibility in the provisioning of available spectral resources. We address the challenges of optimal routing level allocation of wavelengths subject to maximizing spectral efficiencies. In so doing, we also consider the impact of various physical impairments. We introduce a quality factor-based tool (Q-factor) that aids in choosing op-timal routes. Ultimately, The tool is later incorporated into a proposed resource allocation framework's algorithms, which focus on ensuring spectral efficiency and overall network energy-efficient operation. Overall, we conclude that the pro-posed transport network architecture is relatively efficient regarding energy and spectral efficiencies. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Unforgettable Password Generation Using LoRA Fine-Tuned Large Language Model

Unforgettable Password Generation Using LoRA Fine-Tuned Larg...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Sameer, Mohammed Sudharsan, K. Benazir Begum, A. Hindustan Institute Of Technology And Science Dept.of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

In response to the challenges posed by complex and hard-to-remember passwords in traditional authentication systems, this research introduces an innovative approach leveraging Low Rank Adaption (LoRA) fine-tuned Large Language Models (LLMs) to generate natural language-like strong passwords. Focused on striking a balance between usability and security, proposed system methodology employs smaller-sized pre-trained LLMs, adapted using LoRA fine-tuning, to produce passwords that are both strong and memorable. By addressing the limitations of current password generation practices, proposed system framework aims to encourage user adoption of secure practices, contributing to a positive societal impact through improved cybersecurity hygiene. The proposed solution involves deploying a fine-tuned LLM and serving them through an frontend web UI which emphasizes resource efficiency scalability of deployment of such model, and ease of using the generated passwords in real life authentication purposes fostering good cybersecurity practices among non-technical users. © 2024 IEEE.

关键词： Authentication

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