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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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DRL Based Multi-objective Resource Optimization Technique in a Multi-cloud Environment 4th

DRL Based Multi-objective Resource Optimization Technique in...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Department of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science Baba Farid College Punjab Bathinda India Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Akal University Talwandi Sabo Punjab India

ISBN: (纸本)9783031488870

The concept of multi-cloud becomes interesting progressively to cloud users because of its high response time, flexibility, high throughput, and reliability. But at the ground level, the concept of multi-cloud creates many challenges for researchers. The request of users and the multi-cloud environment is heterogeneous now a day. To work in this kind of environment required an intelligent system. Researchers are doing well in this field to make the whole process very flexible by providing an intelligent environment. The proposed multi-objective resource optimization deep reinforcement learning (MOROT-DRL) model uses the Q-learning technique of Deep Reinforcement Learning (DRL) to allocate resources in a multi-cloud environment. It includes a service analyzer for analyzing the requests and MET(Minimum Execution Time)algorithm used for scheduling the task according to execution time and then enhanced flower pollination allocate the optimized resources for the demanded request. The comparison of the proposed model is done with simulation results of MOROT and neural network model and also implemented on GoCS real dataset of google. The proposed model gives better results when compared based on energy, CO2, and cost. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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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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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach 3

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ambooj Khan, Ihtiram Raza Agarwal, Parul Kaur, Harleen School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Bvicam Department of Computer Science and Application New Delhi India

ISBN: (纸本)9798331518592

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10-20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of 'Alzheimer's disease' (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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Design of a Machine Learning based Data Analytics Engine to Improve Decision Making Processes 5

Design of a Machine Learning based Data Analytics Engine to ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

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

ISBN: (纸本)9781665474672

Analytical Hierarchy Processing (AHP) assists in estimation of input-specific weights for improving system-level decision making operations. The process initially decides a goal, and then uses different criterion & alternatives to achieve it. But original AHP model is not efficient for large-scale systems, which limits its capability under real-time use cases. To overcome this issue, various Machine Learning based techniques are proposed, and each of them has their own set of limitations. Techniques with higher efficiency also showcase high complexity, while simplistic optimization techniques have limited performance levels. To overcome these limitations, this text proposes design of an efficient AHP model with low complexity via integration of (GWO), and iterative learning (IL) operations. The proposed model initially performs context-based correlation between input parameter sets. These correlation values are used to modify the AHP decisions, which are tuned by GWO process. Results of Grey Wolf optimization this GWO process are optimized via IL based tuning, which incrementally modifies the correlation values for optimum efficiency levels. Due to which the proposed model showcases low complexity, while maintaining higher efficiency when compared with the original AHP process. The proposed model was evaluated on multiple datasets including housing, image processing, and employment scenarios. Based on this evaluation it was observed that the proposed model showcased 8.5% higher accuracy, 6.4% higher precision, with 4.5% 1ower delay when compared with original AHP and standard AHP-optimization techniques. Due to this improvement, the proposed model is capable of deployment for a wide variety of real-time scenarios. © 2023 IEEE.

关键词： Machine learning

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A Deep Learning-based Transfer Learning Approach Fine-Tuned for Detecting Diabetic Retinopathy 5

A Deep Learning-based Transfer Learning Approach Fine-Tuned ...

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5th International Conference on Innovative Data Communication Technologies and application, ICIDCA 2024

作者： Singh, Devendra Dobhal, Dinesh C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Department of Computer Application Graphic Era Deemed to be University Dehradun248002 India

Diabetics have blood sugar levels that are abnormal. Because of this, their eyesight can be affected. Untreated diabetes has the potential to result in permanent blindness, a grave complication. Among the severe consequences, early detection stands out as a critical hurdle for ensuring effective treatment. Regrettably, accurately assessing fundus images relies on human expertise to determine the stage of diabetic retinopathy. A simplified detection process could greatly benefit a vast population. Leveraging convolutional neural networks (CNN), which have demonstrated efficacy across various domains, diabetic retinopathy identification has seen advancements. The possibility of preventing complete, irreversible blindness lies in timely identification, underscoring the need for a dependable screening mechanism. To enhance the efficiency of screening and reduce errors that plague current models, a novel system has been introduced, capable of categorizing distinct diabetic retinopathy stages. This study focuses on examining datasets of Indian diabetic retinopathy images, encompassing four classes: Microaneurysms (MA), Soft Exudates (SE), Hard Exudates (EX), and Hemorrhages (HE). By employing the proposed VGG16 architecture for feature extraction and the Logistic Regression classifier for classification, an impressive accuracy of 90.4% was achieved. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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6G WIRTinX Subnetworks in context of ICN

6G WIRTinX Subnetworks in context of ICN

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Saha, Kundan Kanti Ray, Sangram Dasgupta, Mou National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India National Institute of Technology Raipur Department of Computer Application Chhattisgarh Raipur India

ISBN: (纸本)9798331516666

This article provides a vision of combining Wireless Isochronous Real Time (WIRT) in-X Subnetworks with the Information Centric Networking framework. Here, the advantages of ICN over traditional IP-based networks are discussed in the context of WinXS implementation. A novel privacy preserving naming scheme along with an ICN packet structure and associated communication protocol are proposed for WinXS. The proposed scheme is tested on the AVISPA and NS3 simulation tools and it is found that the scheme satisfies the WinXS security and efficiency requirements, respectively © 2024 IEEE.

关键词： Internet protocols

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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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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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