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

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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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A Survey of Artificial Intelligence in Fashion

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IEEE Signal Processing Magazine 2023年第3期40卷 64-73页

作者： Chen, Hung-Jen Shuai, Hong-Han Cheng, Wen-Huang Hsinchu300 Taiwan Department of Computer Science and Information Engineering Taipei Taiwan

The fashion industry is on the verge of an unprecedented change. Fashion applications are benefiting greatly from the development of machine learning, computer vision, and artificial intelligence. In this article, we present an overview of three major topics of fashion and associated state-of-the-art techniques: 1) fashion analysis, including popularity prediction and fashion trend analysis;2) fashion recommendation, including fashion compatibility and outfit matching;and 3) fashion synthesis, including makeup transfer and virtual try-on. Problem formulations, method comparisons, and evaluation metrics are illustrated for each topic of fashion research. Additionally, promising directions for development in each area are outlined to inspire future research. © 1991-2012 IEEE.

关键词： Artificial intelligence

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Sentimental Analysis-Based Recommended System for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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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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ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2077-2088页

作者： Jayabalan, Divya Elango, Sivasankar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli 620015 India

Dengue is prone to cause frequent disease outbreaks. An early outbreak prediction based on the climatic variables is essential to control the disease’s spread and avoid possible outbreaks. Existing non-seasonal outbreak classification uses a partition-based clustering method for outlier removal;however, a standard outlier detection with improved prediction performance is needed to detect outbreaks. We develop ICE-VDOP model, an Integrated Clustering and Ensemble machine-learning model for Vector-borne Disease Outbreak Prediction to classify outbreaks based on non-seasonal climatic characteristics for Bangkok and Bangladesh datasets. The work begins with identifying the clustering approach that generates a refined dataset ensuring the data points’ inherent structure is preserved and does not alter the association of climatic variables and Dengue Incidence. The partitioning-based clustering methods modify the direction of association between climatic variables and Dengue Incidence after outlier removal as it removes the essential data points. The comparative results shows that BIRCH (balanced iterative reducing and clustering hierarchy), a hierarchical clustering technique for outlier removal captures finer non-seasonal climate variables (temperature, rainfall and relative humidity) and gradient boosting (GB) achieves a prediction accuracy of 97% for Bangkok and 98% for the Bangladesh datasets. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Binary classification BIRCH Dengue Gradient boosting Outlier detection Vector-borne disease outbreak prediction

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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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EUR/USD Forex Market Prices Prediction Using MLP 16

EUR/USD Forex Market Prices Prediction Using MLP

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Diab, Raed Nohra, Alain Atwi, Ahmad Haidar, Gaby Abou Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Department of Computer and Communications Engineering United States

ISBN: (纸本)9798350333510

Neural networks is one of the most developed concepts in artificial intelligence owing to their ability to solve complex computational tasks, and its efficiency in finding solutions. There is a wide range of applications that adopt this technique, one of which is in the financial investment domain. This paper presents an approach to predict foreign exchange market prices using a neural network. It considers MLP techniques using sequential models with dense layer methods. Tests were conducted on one of the forex major pair data sets, EURUSD. The work is divided into 5 parts: prediction for the next minute, hour, day, week, and month resulting in 5 different models. Accordingly, a best-case accuracy of 99% for MLP algorithms is demonstrated. © 2023 IEEE.

关键词： Investments

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