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An Integrated Approach of Efficient Edge Task Offloading Using Deep RL, Attention and MDS Techniques

SN computer science 2024年第6期5卷 681页

作者： Priyadarshni Kumar, Praveen Kadavala, Dhruvan Tripathi, Shivani Misra, Rajiv Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

In Distributed Computation Optimization (DCO) networks, where clients distribute computational jobs among heterogeneous helpers with different capacities and pricing models, efficient task offloading and cost reduction continue to be major issues. In order to tackle this problem, a unique method utilizing Deep Reinforcement Learning (DRL) is presented in this study. The DRL algorithm allows clients to independently identify the best method of action without requiring prior knowledge of network dynamics since it adjusts to the dynamic and stochastic nature of DCO environments. In distributed computing environments, the combination of Maximum Distance Separable (MDS) and DRL approaches provides a stable framework for task offloading. The suggested method finds the best policy for helper selection and task offloading by combining reward estimation, action selection, environment modeling, and iterative learning approaches. In particular, energy consumption, density, and total rewards show that the DRL algorithm performs better in experimental evaluations than traditional techniques like Q-learning and random selection. The algorithm’s performance is further improved by the integration of DQN with the attention mechanism, highlighting its potential to completely transform the efficiency of DCO networks. This work highlights how DRL approaches have a significant effect on network operations optimization and offers insightful information about future developments in distributed computation paradigms. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning Distributed computation optimization Edge computing MDS techniques Task offloading

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Decision Tree Ensemble Approach for Recidivism Risk Classification Among Heinous Crime Convicts

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SN computer science 2024年第1期5卷 195页

作者： Singh, Aman Mohapatra, Subrajeet Department of Computer Science Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

Early prediction of violent recidivism is one of the most essential tasks of the judiciary system. Being able to accurately forecast violent recidivism in offenders with heinous crime conviction can facilitate judiciary in taking decisions about sentencing, release, parole, and probation. Additionally, because violent offenders have a higher recidivism rate than nonviolent offenders, the development of intelligent risk assessment tools for early prediction of violent recidivism becomes highly essential. However, few such approaches exist, and their efficacy in the Indian context is often limited. As a result, in the present work, the aim is to develop an computational learning-based risk assessment tool for early prediction of violent recidivism among criminals with heinous crime convictions. In particular, we classify individual offenders as high, low, or moderate risk based on a variety of factors such as anger level (a psychological emotion), demography, and psycho-social factors. Effectiveness of different machine learning models ***, PNN, SVM, KNN, LDA have been simulated for recidivism risk assessment of offenders. The current findings demonstrate that violent recidivism at the individual level can be anticipated using a bagged tree ensemble classifier achieving maximum accuracy of 89 % . © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Ensemble learning Machine learning Quantitative risk assessment Violent recidivism

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Study on Implementation of Shor’s Factorization Algorithm on Quantum computer

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SN computer science 2024年第4期5卷 413页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

The fundamental security aspect of the classical crypto-system depends on integer factorization and discrete logarithm problems. The quantum factorization problem is a crucial problem in quantum computing as it has significant implications for cryptography and security. Shos’s paper on “Polynomial-time algorithms for discrete logarithms and factoring on a quantum compute” has become a threat to the classical crypto-system, influencing many researchers to work on factorization problems using quantum computing. Quantum computers (QC) can be essential in running different factorization algorithms in polynomial time. However, practical implementation on larger numbers is still a major challenge due to the requirement for error correction and massive quantum devices. Although the quantum factorization issue puts traditional cryptographic systems at risk, it also opens up new possibilities for quantum communication and encryption. The challenge of factorization opens up an entirely new field for research into quantum communication protocol security, including quantum key distribution and the development of quantum-resistant cryptographic systems. This paper surveys the various quantum algorithms for factoring a number into prime integers. We present a simulation study of the Quantum factorization algorithm to determine the period of a process. Turning the factoring problem into the challenge of determining a function’s period is the essential strategy of practical implementation using the quantum circuit. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Factorization Quantum Fourier Transformation Quantum period finding Quantum phase estimation Shor’s algorithm

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Comprehensive Overview and Analytical Study on Automatic Bird Repellent Laser System for Crop Protection

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Journal of Harbin Institute of Technology(New Series) 2024年第1期31卷 38-53页

作者： Sireesha Abotula Srinivas Gorla Prasad Reddy PVGD Mohankrishna S Department of Computer Science and Engineering GITAM Deemed to be UniversityVisakhapatnam 530045India Department of Information Technology Andhra UniversityVisakhapatnam 530003India Department of Computer Science and Systems Engineering Andhra UniversityVisakhapatnam 530003India

Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.

关键词： Bird repellent crop protection IoT UAV Deep Learning

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Chain-of-Factors: A Zero-Shot Prompting Methodology Enabling Factor-Centric Reasoning in Large Language Models 23

Chain-of-Factors: A Zero-Shot Prompting Methodology Enabling...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Hussain, Musarrat Ur Rehman, Ubaid Nguyen, Tri D.T. Lee, Sungyoung Kim, Seong Tae Bae, Sung-Ho Kim, Jung Uk UiT The Arctic University of Norway Department of Computer Science Tromso Norway Kyung Hee University Dept. of Computer Science & Engineering Yongin Korea Republic of Kyung Hee University Department of Computer Science and Engineering Yongin Korea Republic of

ISBN: (纸本)9798350374889

Large language models (LLMs) have significantly improved numerous natural language processing tasks. However, their performance relies heavily on the provided instructions or prompts. Recently, several prompting methodologies have been developed to enhance the reasoning abilities of LLMs. Notably, the Chain-of-Thought (CoT) approach provides examples that help break down tasks into sub-steps, resulting in more accurate solutions. However, the process of generating detailed examples may not be user-friendly, as end users prefer providing task descriptions rather than a set of examples. In this study, we introduce Chain-of-Factors (CoF), an innovative zero-shot prompting methodology that incorporates task-specific instructions as a chain of factors into the prompt, aimed at enhancing the factor-centric reasoning abilities of LLMs. Experiments on three LLMs, including ChatGPT-3.5, Gemini, and GPT-4, show performance improvements ranging from 0.01% to 40.2% in accuracy on various symbolic reasoning and logical reasoning tasks compared with zero-shot and few-shot CoT. In summary, CoF enhances LLMs' reasoning abilities by including task-specific steps and instructions, while also decreasing the necessity for fine-tuning specific to each task. © 2024 IEEE.

关键词： Natural language processing systems

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Deep Learning-based Hybrid Recommendation System for Safe Approach to Curated Cosmetics 3

Deep Learning-based Hybrid Recommendation System for Safe Ap...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Rohitha Chowdary, G. Tharun, J. Sivaiah, G. Raveena, S. Surendran, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Insititute of Medical and Technical Science Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

In the rapidly evolving cosmetics market, it is imperative to prioritize customer safety while offering personalized recommendations. This study introduces a hybrid recommendation system for cosmetics, using deep learning to combine the advantages of collaborative filtering with content-based filtering. The system assesses user preferences, skin types, and ingredient sensitivities, recommending solutions that are both effective and safe. The system proposes an integration of user-based and item-based collaborative filtering methodologies with deep neural networks to effectively elucidate intricate links among individuals, items, and their characteristics. This hybrid technique facilitates the system's ability to propose goods that provide both aesthetic appeal and skin safety, therefore fostering a healthier and more confident lifestyle. The system has an impressive accuracy of 92.5% in recommending suitable cosmetic products for individual clients. This enhanced accuracy reduces the likelihood of negative reactions and elevates customer satisfaction via personalized suggestions. © 2025 IEEE.

关键词： Contrastive Learning

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military Technology Object Recognition Situational Awareness YOLOv8

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Robust And Discriminant Local Color Pattern (RADLCP): A novel color descriptor for face recognition

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International Journal of Hybrid Intelligent Systems 2024年第1期20卷 23-39页

作者： Karanwal, Shekhar Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

In [1] Karanwal et al. introduced the novel color descriptor in Face Recognition (FR) called as Fused Local Color Pattern (FLCP). In FLCP, the RGB color format is utilized for extracting features. From R, G and B channels, the MRELBP-NI, 6 × 6 MB-LBP and RD-LBP are imposed for feature extraction and then all are integrated to form the FLCP size. FLCP beats the accuracy of various methods. The one major shortcoming observed in [1] is that the basic format RGB is used for extracting features. Literature suggests that other hybrid formats achieves better recognition rates than RGB. Motivated from literature, the proposed work uses the hybrid color space format RCrQ for feature extraction. In this format R channel is taken from RGB, Cr channel is taken from YCbCr and Q channel is taken from YIQ. On R channel, MRELBP-NI is imposed for extracting features, On Cr channel 6 × 6 MB-LBP is imposed and on Q channel RD-LBP is imposed for extracting features. Then all channel features are joined to build the robust and discriminant feature called as Robust And Discriminant Local Color Pattern (RADLCP). Compression and matching is assisted from PCA and SVMs. For evaluating results GT face dataset is used. Results proves the potency of RADLCP in contrast to gray scale based implemented descriptors. RADLCP also beats the results of FLCP. Several literature techniques are also outclassed by RADLCP. For evaluating all the results MATLAB R2021a is used. © 2024 - IOS Press. All rights reserved.

关键词： classification color feature compression dataset Feature extraction global feature local feature

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Autonomous Vehicle Lane Following Network based on Multimodal Sensor Fusion and LiDAR Reconstruction in Extreme Lighting and Snowing Environments 4

Autonomous Vehicle Lane Following Network based on Multimoda...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Padmapriya, J. Meera, V. Murugavalli, S. Karthikeyan, L. Panimalar Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Mechanical Engineering Chennai India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387063

The self-driving car industry is gaining attention for its role in motion planning technology. Deep learning approaches have been implemented to plan autonomous vehicles' motion, but their effectiveness depends on the data used in training. Despite advancements, fully autonomous vehicles still face limitations, such as lighting-related issues affecting camera image accuracy, and LiDARs are negatively impacted by challenging weather circumstances like snow in varied snowfall conditions, thus becoming imperative to precisely control the motion of the Autonomous Vehicle. To address this, an architecture based on deep-learning networks is proposed for lane-following on roads, consisting of LiDAR De-snowing, Multi-modal Sensor Fusion, VGG16, and gated recurrent units (GRUs). The Point Reconstruction Network (PR-Net) rebuilds points from neighbors, while the Reconstruction Difficulty Network forecasts reconstruction difficulty. Radar measurements and de-snowed LiDAR point clouds are projected onto the front-view image plane, fed into VGG16 and GRU output layers, and processed into two fully connected layers for steering and speed parameters estimation. © 2024 IEEE.

关键词： Snow

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A cross entropy and whale optimization algorithm based image segmentation for aerial images

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International Journal of Information Technology (Singapore) 2024年 1-15页

作者： Ahmed, Saifuddin Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Cachar Silchar 788010 India

There are several image segmentation techniques for image processing such as, thresholding, region based, edge based, watershed etc. However, the multilevel thresholding based image segmentation is the most widely used approach. This approach mostly suffers high computational complexity with the increasing number of threshold levels. The researchers have been using optimization techniques to resolve this issue. This paper presented a multilevel thresholding technique for segmenting aerial images using Whale Optimization Algorithm (WOA). It uses Minimum Cross Entropy (MCE) as an objective function for selecting a set of optimum threshold values. The presented method is evaluated by using a set of standard aerial images. The evaluated performance of the proposed technique is compared with Bacterial Foraging Optimization (BFO), Moth-flame Flame optimization (MFO) and Particle Swarm Optimization (PSO). The overall performance and the quality of segmented images are evaluated based on the following parameters, Average Mean Structural Similarity Index (MSSIM), Average Feature Similarity Index (FSIM), Average Peak Signal to Noise Ratio (PSNR), average mean objective functions values and average CPU rum time values. In terms of quality of the segmented images based on MSSIM, FSIM and PSNR values the presented method performs better than other methods and we found the average CPU run time also to a considerable range. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cross entropy Image segmentation Multilevel thresholding Optimization technique Whale optimization algorithm

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