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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Das, Manab Kumar Bhattacharjee, Swapnamoy Mondal, Saikat Goswami, Soham Department of Computer Application and Science IEM-UEM Kolkata India Department of CSE MCKV Institute of Engineering West Bengal Howrah India

ISBN: (纸本)9789819776023

The basic building blocks of the human body are cells, and cancer is defined as an abnormal proliferation of bodily cells. There are however numerous other causes of cancer. Cancer has emerged as one of the world's most perilous causes of death in recent years. The fatality rate of Breast Cancer (BC) is increasing globally on a daily basis. The majority of women worldwide lost their lives as a result of BC. In 2020, an estimated 2,261,419 new breast cancer cases for women were reported worldwide. Histopathology image data analysis can be used to diagnose or forecast breast cancer in the majority of instances, but this type of analysis at the genetic level is very time-consuming and expensive. In this study, the main objective is to examine various well-known Machine Learning (ML) techniques, including Logistic Regression, Random Forest, KNN, and Decision Tree, in order to investigate BC. This proposed technique introduces a hybrid approach based on the overlap value of particular features and also attempts to calculate the accuracy at every stage. Additionally, thoroughly examine the correctness of the suggested system in comparison to existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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MLTPDFS: Design of Machine Learning Model for Temporal Analysis of Progressive Diseases via Facial Scans 5th

MLTPDFS: Design of Machine Learning Model for Temporal Analy...

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5th International Conference on Information Systems and Management Science, ISMS 2022

作者： Dandekar, Pranali Rahul Pethe, Yoginee Surendra Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9783031311529

Edge analysis, colour variation analysis, texture change analysis, etc. can identify multiple health conditions in facial scans. Identifying these diseases requires individual image processing models. Temporal facial analysis can identify stress, anxiety, happiness, high blood pressure, diabetes, and other health conditions. Most of these models use independent Machine Learning (ML) components for disease identification, limiting real-time scalability. Some of these models combine ML components, but their clinical prediction and analysis is limited. This text proposes a novel Machine Learning Model for Temporal analysis of progressive diseases via Facial Scans. The proposed model trains CNNs to identify Stress, Anxiety, Heart Conditions, Cancer, and Brain abnormalities. Each disease type requires its own CNN Model. VGGNet-19 predicts stress and anxiety, YoLOV3 identifies brain abnormalities, ResNet-101 predicts heart conditions, and ResNet-152 identifies cancer levels. These models were chosen for their accuracy, precision, and AUC for different diseases. The proposed model combines CNNs using bio-inspired machine learning. The combined model’s hyper-parameters are tuned based on real-time performance metrics. An incremental learning layer improves these metrics by incorporating continuous learning. Due to these operations, the proposed model improves classification accuracy by 5.4%, precision by 6.5%, recall by 8.3%, and AUC by 4.5%. The model performed faster than existing methods due to machine learning, which reduces training and validation delays, making it useful for real-time clinical use cases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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A Survey of Privacy Preserving Methods based on Differential Privacy for Medical Data 7

A Survey of Privacy Preserving Methods based on Differential...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Yan, Haicao Yin, Menghan Yan, Chaokun Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application Kaifeng China

ISBN: (纸本)9798350372328

Using deep learning and federated learning in the medical domain has facilitated convenience in diagnosing and treating patients. However, the current privacy leakage problem has made patients more cautious about personal privacy security. Privacy security has become an urgent issue. Differential privacy is a cryptography technique that makes it impossible for attackers to identify personal data by adding noise to the training process. In this work, we first provide a complete overview of differential privacy technologies. Then, we will review existing differential privacy works in the medical field. Finally, we analyze the existing problems of differential privacy and propose possible avenues for further research. © 2024 IEEE.

关键词： Deep learning

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Ensemble Knowledge Distillation for Federated Semi-Supervised Image Classification

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Tsinghua Science and Technology 2025年第1期30卷 112-123页

作者： Ertong Shang Hui Liu Jingyang Zhang Runqi Zhao Junzhao Du School of Computer Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of EducationXi’an 710126China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126China

Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.

关键词： federated learning semi-supervised learning federated semi-supervised learning knowledge distillation

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach 6

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ansar, Syed Anas Aggarwal, Shruti Arya, Swati Sayeed, Mohd Asim Soni, Nupur Pathak, Prabhash Chandra School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350304480

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices. © 2023 IEEE.

关键词： artificial intelligence blockchain cybersecurity machine learning national security quantum computing zero-trust security

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Improving energy efficiency in scalable WSNs through IoT-driven approach

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Singh, Omkar Kushwaha, Ajay Shriram Sharma, Col. Rahul Kumar, Lalit National Institute of Fashion Technology Bihar Patna India Department of Computer Science & Application Sharda School of Computing Sciences & Engineering Sharda University UP Greater Noida India School of Computer Science & Engineering ILM University Greater Noida India

A novel QoS-Driven Optimization for Energy-Efficient Clustering Routing (QDOEECR) protocol tailored for IoT-based Wireless Sensor Networks (WSNs), addressing the critical challenge of energy efficiency in sensor nodes with limited battery life. The protocol’s innovative approach lies in its advanced techniques for optimizing the selection of sub-sink nodes and identifying the optimal path to the sink node, ensuring improved adherence to Quality of Service (QoS) standards. Performance evaluations, conducted through MATLAB simulations, demonstrate the QDOEECR protocol's superior performance, achieving significant advancements over existing routing protocols. These include 30–35% reduction in end-to-end delay, 25–30% improvement in throughput, 35–40% decrease in energy consumption, 28–32% reduction in packet loss, and 32–38% extension in network lifetime. These results establish QDOEECR as a groundbreaking solution that not only enhances energy efficiency but also elevates network performance metrics beyond the capabilities of current state-of-the-art protocols like CBCCP, HCSM, and ZEAL. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： CBCCP HCSM IoT QDOEECR WSN ZEAL

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Intelligent Farming: IoT-Powered System for Efficient Soil Condition Analysis 13

Intelligent Farming: IoT-Powered System for Efficient Soil C...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Sharma, Nandini Singh, Shiv Shanker Khenwar, Medha Gupta, Priyanka Gla University Department of Computer Engineering and Application Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Smart farming technology allows farmers to control and analyse variables in real-time, helping them optimize crop growth and manage their operations more efficiently. The use of specialized sensors, data collection tools, and advanced analytics in Internet of Things (IoT) systems is helping to bridge the gap between physical farm conditions and digital monitoring. In this study, we aim to develop a system that monitors soil conditions to support smart farming. The system provides farmers with live data on key environmental factors, such as temperature, humidity, soil moisture, and soil temperature, to improve crop yield and product quality. The IoT setup for this intelligent farming solution includes components like the DHT11 and humidity sensors, a DS18B20 temperature probe, LDR, water pump, and a 12V LED strip, all controlled by a NodeMCU. When the system is activated, it continuously monitors soil and environmental conditions and sends this information to the cloud for real-time access. If the soil moisture falls below a set threshold, the system automatically triggers irrigation. Our experimental results show that this model improves decision-making for managing farming conditions. © 2024 IEEE.

关键词： Smart agriculture

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Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain 3

Quantum Odyssey: Traversing the NISQ Era's Quantum Terrain

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Arya, Swati Ansar, Syed Anas Aggarwal, Shruti Babu Banarsi das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350342338

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing. © 2023 IEEE.

关键词： Ethical Implications Noisy Intermediate-Scale Quantum (NISQ) Era Quantum Algorithms Quantum Computing Quantum Error Correction (QEC)

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QoS aware task scheduling and congestion avoidance in fog enabled car parking systems

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4787-4795页

作者： Dhananjaya, M.K. Sharma, Kalpana Chaturvedi, Amit Kumar Department of Computer Science Bhagwant University Ajmer India Department of Computer Science and Application Bhagwant University Ajmer India Department of MCA Govt. Engineering College Rajasthan Ajmer India

When multiple cars compete for the same parking space, congestion arises. As it turns out, individuals waste an extensive amount of time looking for parking spaces, which increases fuel consumption and emissions. A few researchers proposed a fog-based smart car parking approach to address the drawbacks of cloud-based approaches. The researchers were unable to reach the global optimum for picking an empty parking space, and they neglected to consider quality of service (QoS) when a fog node can’t process a request. Therefore, to achieve such goals, we proposed QoS aware task scheduling and congestion avoidance in fog enabled car parking systems. This approach breaks down into three layers: the data layer, the fog layer, and the cloud layer. Data from car parking lots are gathered in the data layer using IoT devices. The relevant fog node will receive the gathered data in the fog layer. The task will be scheduled to a different suitable fog node using the multi-objective artificial flora optimization (MoAFO) algorithm. The available free parking spaces in each fog node are also estimated using the adaptive coati optimization algorithm (ACOA). The cloud server layer then stores and manages the data that the fog nodes have collected. The Java platform is used to implement the proposed methodology. The energy, time, searching time, and latency metrics are used to evaluate the effectiveness of the recommended approach. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Adaptive coati optimization algorithm (ACOA) Multi-objective artificial flora optimization (MoAFO) algorithm Quality of service (QoS)

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