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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Pawandeep Singh Ughara Sahil Bhardwaj Gaurav Kumar Singh Aditya Kumar Gupta Miransh Jain Shrey Parashar Dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

As cloud computing continues to grow in significance within the IT landscape, it brings a myriad of security challenges that necessitate thorough investigation and innovative solutions. This comprehensive review aims to explore the security vulnerabilities associated with cloud computing, such as data protection, access management, encryption practices, and identity handling. The objective of the proposed work is to critically analyze existing strategies and recommend robust practices to mitigate risks, thereby bolstering the security frameworks of cloud systems. By synthesizing research from various sources, this study not only elucidates the pressing security concerns within cloud computing but also underscores the need for enhanced security protocols and standards. Ultimately, this analysis serves as a foundational tool to guide ongoing efforts towards creating more resilient cloud security architectures.

关键词： Technological innovation Reviews Heuristic algorithms Cloud computing security Standards organizations Organizations Encryption Security Stakeholders Reliability

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Detectiona and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learing

arXiv

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arXiv 2025年

作者： Mollick, Abu Ahnaf Tamim, Abdullah Rahman, Mahfujur Dristi, Nosin Anjum Asadujjaman, D.M. Hossen, Takbir Dept. Of Computer Science and Engineering Varendra University Rajshahi Bangladesh

A mutation in the DNA of a single cell that compromises its function initiates leukemia. This leads to the overproduction of immature white blood cells, which encroach upon the space required for the generation of healthy blood cells. Leukemia is treatable if identified in its initial stages. Nonetheless, its diagnosis is both arduous and time-consuming. In this study, a novel approach for diagnosing leukemia across four stages—Benign, Early, Pre, and Pro—utilizing deep learning techniques. We employed two Convolutional Neural Network (CNN) models: MobileNetV2 with an altered head and a bespoke model. The custom model has multiple convolutional layers, each paired with corresponding max pooling layers. We utilized MobileNetV2 with ImageNet weights, and the head was adjusted to integrate the final results. The utilized dataset is a publicly available collection of blood cell smear images titled "Acute Lymphoblastic Leukemia (ALL) image dataset", and then used the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset. Which attained an accuracy of 98.6% with the custom model, while MobileNetV2 achieved a superior accuracy of 99.69%. The pretrained model exhibited encouraging results and an increased likelihood of real-world *** Codes 68T07 © 2025, CC BY.

关键词： Convolutional neural networks

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Smart Garden Watering and Lighting System Integrated with Machine Learning Model to Maintain Urban Gardens

Smart Garden Watering and Lighting System Integrated with Ma...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Arsheyee Shahapure Rishika Siddha Tanmay Tewary Aniruddha Shahapure Shamla Mantri Aparna Kamble dept. computer science and engineering MIT-WPU Pune India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Garden maintenance may be a difficult task, particularly in urban settings. Many people are not skilled enough to determine their plants’ precise requirements for lighting and watering. Conventional gardening techniques frequently result in underwatering or overwatering, wasting resources, and producing unhealthy plants. Our completely automated smart garden watering and lighting system offers a fully automated solution to these problems. With its sophisticated sensors, this system keeps an eye on the quantity of moisture in the soil and the amount of sunshine it receives, making sure plants get the correct amount of both for healthy growth. Traditional gardening involves guesswork and physical effort, which our solution avoids by automating the irrigation and lighting operations. You may have the advantages of a flourishing garden without the trouble of ongoing upkeep with our intelligent gardening system. Our method offers a dependable and effective solution to care for your plants and improve your urban green space, regardless of experience level.

关键词： Support vector machines Irrigation Lighting Moisture Machine learning Soil Sensor systems Real-time systems Maintenance Internet of Things

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Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Estimating Post-Synaptic Effects for Online Training of Feed...

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Neuromorphic Systems (ICONS), International Conference on

作者： Thomas M. Summe Clemens JS Schaefer Siddharth Joshi Dept. of Computer Science and Engineering University of Notre Dame

ISBN: (数字)9798350368659

ISBN: (纸本)9798350368666

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%; OTPE - 75.2%; BPTT - 78.1%).

关键词： Training Adaptation models Neuromorphics Computational modeling Scalability Spiking neural networks Approximation algorithms Real-time systems Encoding Streams

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An In-dept. Exploration of Advanced Driver Drowsiness Detection Systems for Enhanced Road Safety

An In-Depth Exploration of Advanced Driver Drowsiness Detect...

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computer, Communication and Control (IC4), International Conference on

作者： A. Abirami S Bhuvaneswari M Vishnuvardhan Yadam Karthik Saisuraj Shankar Dept. of Computer Science and Engineering Assistant Professor of Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Student of Easwari Engineering College Chennai India

Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods for detecting driver fatigue. This survey paper presents an overview of approaches used to identify driver fatigue. These approaches can be grouped into three categories: Behavioural, Vehicular and Physiological parameters. Behavioural parameters, such as eye closure percentage, blink rate patterns, yawning, head movements and facial expressions offer intrusive techniques that examine the physical state of the driver’s body and provide information about their drowsiness level. Vehicular parameter-based techniques evaluate drowsiness by observing vehicle behaviour such as lane deviations, pedal usage, and unpredictable steering movements. Physiological indicators are also employed to detect tiredness. The paper also discusses the methodology employed in the research study including image processing techniques classifiers used and measurement metrics utilized, proposed methodology of fatigue detection with adding some extra features and Lastly the paper presents conclusions based on a study that can guide future researchers in identifying potential areas for further exploration in this field.

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Stacking Ensemble Technique to Predict Cervical Cancer Using Hyperparameter Tuning and Feature Selection

Stacking Ensemble Technique to Predict Cervical Cancer Using...

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International Conference on electrical, computer and Communication engineering (ECCE)

作者： Md. Tofael Ahmed Bhuiyan Khandaker Mohammad Mohi Uddin Md. Rakibul Islam Md. Habibullah Belali Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Cervical cancer is the primary cause of premature mortality for women in underdeveloped countries, where it causes approximately 85% of cases. Better outcomes from treatment and preventative measures depend on early risk factor assessment. A thorough prediction model based on preliminary testing data and individual medical records is presented in this study. The risks were analyzed using algorithms such as Random Forest (RF), Decision Tree (DT), and AdaBoost. SelectKBest, XGBoost, and Chi-Square are examples of feature selection approaches that improved feature extraction, improving performance and interpretation. The algorithms for machine learning that were used were LR, LGBM, SVM, KNN, and Naive Bayes. To attain the highest accuracy, grid search and cross-validation were used for hyper parameter optimization. Stacking ensembles combined RF, DT, and AdB, achieving 99.443% accuracy. Ensemble approaches, particularly when resolving decreases in features and disparity in class, helped to make clinical decisions and accurately predicted the risk of cervical cancer.

关键词： Radio frequency Accuracy Machine learning algorithms Stacking Feature extraction Ensemble learning Decision trees Cervical cancer Random forests Tuning

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Microwave Imaging Based Lesion Monitoring During Infarct Ablation: Feasibility Study

Microwave Imaging Based Lesion Monitoring During Infarct Abl...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Gaddam, Sunil Samaddar, Poulami Gopalakrishnan, Keerthy Aedma, Keirthana Damani, Devanshi Shivaram, Suganti Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine Mayo Clinic RochesterMN United States Department of Cardiology Mayo Clinic RochesterMN United States Department of Pharmacology Mayo Clinic RochesterMN United States Dept. of Electrical and Computer Engineering North Dakota State University FargoND United States Dept. of Electrical Engineering and Computer Science South Dakota Mines Rapid CitySD United States Dept. of Computer Science University of Wisconsin-La Crosse La CrosseWI United States Department of Radiology Mayo Clinic RochesterMN United States

ISBN: (纸本)9781665442282

Sudden cardiac death rate post-myocardial infarction due to arrhythmia is increasing. Cardiac ablation of infarcts is shown to reduce arrhythmogenic events. Current methods are inadequate to monitor the lesions during infarct ablation. Therefore novel noninvasive tools are necessary to provide real-time monitoring of ablation and to titrate the ablative power to reduce arrhythmogenicity. The purpose of this simulation study was to demonstrate the feasibility of microwave imaging (MWI) to monitor the temperature profile of the lesions during infarct ablation. A 2D model of the left ventricle was used with an infarct region of interest for microwave imaging electromagnetic simulation at 915 MHz using known myocardial dielectric properties and its temperature dependence. Distorted Born Iterative Method (DBIM) was used to reconstruct the relative permittivity that is mapped to the temperature field and compared within the infarct region. Good agreement was observed between the reconstructed and assigned temperature field within the infarct region. The study demonstrates the feasibility of using MWI for the non-invasive assessment of infarct tissue temperatures during ablation. © 2023 IEEE.

关键词： Ablation

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Textual Authenticity in the AI Era: Evaluating BERT and RoBERTa with Logistic Regression and Neural Networks for Text Classification

Textual Authenticity in the AI Era: Evaluating BERT and RoBE...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Layth Rafea Hazim Oguz Ata Dept. of Electrical and Computer Engineering Altinbas University Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Dept. of Software Engineering Altinbas University Istanbul Turkey

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models’ robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications.

关键词： Training Logistic regression Ethics Accuracy Text categorization Writing Transformers Data models Feedforward neural networks Artificial intelligence

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Outage Analysis for a HAPS-Mounted IRS-Assisted Multi-User Communication System

Outage Analysis for a HAPS-Mounted IRS-Assisted Multi-User C...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Ahmed M. Benaya Mohamed S. Hassan Mahmoud H. Ismail Taha Landolsi Mohamed I. AlHajri Dept. of Electrical Engineering American University of Sharjah Sharjah UAE Dept. of Computer Science and Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

In this paper, we propose a high altitude platform station (HAPS)-mounted intelligent reflecting surface (IRS)assisted multi-user communication system. Due to potential blockage or the fact that the users might be located outside of the coverage of the terrestrial base station (TBS), there might not be a direct service link from the TBS to the users. Hence, with the use of the reflected signals from the HAPS-mounted IRS, the TBS is able to provide services to its associated user equipment (UEs). With this proposed scenario, the coverage area can be substantially increased as the HAPS are usually located in high altitudes. We study the outage performance of the proposed system taking into account the UEs distribution in the service area. We derive an approximate closed-form expression for the outage probability under a Rician-faded TBS-HAPS link and a shadowed Rician-faded HAPS-UEs link. Moreover, we validate our derived expression through Monte Carlo simulations considering the effect of different system parameters on the outage probability. Results show that the proposed system can achieve outage probability in the order of 10 −4 up to a service area diameter of 30 km and in the order of 10 −2 for service areas in the range of 40 km in diameter.

关键词： Fading channels Monte Carlo methods Closed-form solutions Communication systems Simulation Rician channels Probability Power system reliability Surface treatment Shadow mapping

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AI-Powered Early Detection of Musculoskeletal Disorders in Garment Industry Operators 2

AI-Powered Early Detection of Musculoskeletal Disorders in G...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Vijayvargiya, Ankit Paliwal, Shruti Gehlot, Naveen Kumar, Rajesh Moran, Kieran School of Health and Human Performance Dublin City University Dublin Ireland Manipal University Dept of Computer Science Engineering Jaipur India Malaviya National Institute of Technology Dept of Electrical Engineering Jaipur India

ISBN: (纸本)9798350370188

The issue of work-related musculoskeletal disorders (WRMSD) is becoming increasingly prominent in occupational health and workplace safety, affecting a growing number of individuals. This pressing issue demands increased attention, especially in crucial sectors like the garment industry. In response to this challenge, our study focuses on developing and evaluating deep learning models to facilitate the early prediction and diagnosis of WRMSD in garment industry workers. This research investigates the potential of several deep learning models and conducts a comparative analysis of their performance metrics. The findings demonstrate that the Bi-directional Gated Recurrent Unit model achieved a remarkable accuracy of 97.19%, surpassing all other deep learning models examined in this study. © 2024 IEEE.

关键词： Long short-term memory

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