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Feature Selection for Classification on High- Dimensional data Using Swarm Optimization Algorithms 7

Feature Selection for Classification on High- Dimensional Da...

7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Pradeep Kumar, D. Bj, Sowmya Kanavalli, Anitha Deeptashree, D. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350343144

Feature selection plays a crucial role in classification by identifying relevant features. Bio-inspired algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, have gained popularity due to their ability to mimic natural processes and explore high-dimensional feature spaces efficiently. The analysis includes evaluating classification accuracy, and computational efficiency as performance metrics. The experimental setup involves dataset selection, choosing a classification algorithm, implementing bio-inspired feature selection, evaluation, parameter tuning, and comparative analysis. The findings provide valuable insights into the effectiveness and efficiency of bio-inspired algorithms for feature selection in classification. The project focuses on reducing high- dimensional datasets to low-dimensional ones using feature selection through classification with swarm optimization algorithms. It aims to validate the effectiveness of the Jellyfish Search algorithm (JSA), Horse Herd Optimization algorithm (HOA), and Binary Bat algorithm (BBA) on real-world datasets. The objectives include evaluating the algorithms' performance, conducting a comprehensive performance analysis, execution of classifiers like RF, DT, KNN, Voting Classifier and SVM, obtaining and tuning the results, and publishing the findings. The research outcomes contribute to the knowledge of feature selection for classification and provide guidance in selecting appropriate algorithms for specific classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Kidney and Kidney Tumour Segmentation from 3D CT Scan using DeepLabv3+

Kidney and Kidney Tumour Segmentation from 3D CT Scan using ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Jariwala, Taashna A. Mehta, Pranavi C. Mehta, Mayuri A. Joshi, Vivek C. Sarvajanik College of Engineering and Technology Department of Artificial Intelligence and Data Science Surat India Sarvajanik College of Engineering and Technology Department of Computer Engineering Surat India R. N. G. Patel Institute of Technology Department of Information Technology Bardoli India

ISBN: (纸本)9798350364866

Segmenting kidney and kidney tumour from CT scan is crucial in combating challenges in the early detection of kidney cancer. Several segmentation methods are available to segment kidney and kidney tumour from 3D CT scan. However, these methods pose several drawbacks, including dependency on pixel-wise classification, limited generalisation, and manual annotation requirements. Hence, this paper introduces a novel kidney and kidney tumour segmentation approach employing encoder-decoder-based architecture. The proposed segmentation approach is assessed against two encoder-decoder-based architectures, namely U-Net and DeepLabv3+. The proposed approach precisely identifies kidney and kidney tumour in a 3D CT scan. Its performance is analysed using the 2023 Kidney and Kidney Tumour Segmentation Challenge (KiTS23) dataset. The evaluation metrics such as dice coefficient and Intersection over Union (IoU) are used to assess the performance. Our results on the KiTS23 dataset show that DeepLabv3+ outperforms U-Net. Thus, the paper discusses the DeepLabv3+ approach in detail. DeepLabv3+ boasts an average improvement of 0.82% in dice coefficient, 1.60% in IoU, 39.28% in loss during training, and 0.94% in dice coefficient, 1.82% in IoU, 44.88% in loss during validation over U-Net. © 2024 IEEE.

关键词： computerized tomography

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Metaverse-Based Order Picking Optimization for Supply Chain of Things 10

Metaverse-Based Order Picking Optimization for Supply Chain ...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Erel-Ozcevik, Muge Bozkaya-Aras, Elif Bilen, Tugce Manisa Celal Bayar University Department of Software Engineering Manisa Turkey BTS Group Istanbul Turkey National Defence University Turkish Naval Academy Department of Computer Engineering Istanbul Turkey Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey

ISBN: (纸本)9798350373011

In recent years, with the rise of e-commerce and digital services, customers' expectations have affected supply chain management operations. The logistics industry reacts proactively by integrating new technologies into its systems to meet changing customer expectations. Among these technologies, a relatively new concept called Supply Chain of Things (SCoT) is an enabling technology for supply chain operations to increase productivity and automation. However, a large number of customer orders and increasing concerns about operating costs challenge efficient management and quality of service in SCoT. In this regard, this paper addresses the order-picking problem of minimizing delivery time and operating costs, including travel costs and total costs to the employer. Metaverse can be considered a feasible solution to address this problem due to its advantages of seamless and intelligent interaction between the physical world and the digital world. In this paper, we present a metaverse-based system architecture and design the network digital twin to enable intelligent real-time management of the SCoT environment. We formulate the order-picking problem and propose a genetic algorithm-based solution to satisfy customer demands on time with minimal operating costs by creating a digital twin of the supply chain system in the Metaverse. The simulation results show that the proposed solution achieves significant gains compared with baseline strategies in terms of the total cost to the employer while keeping order delivery time under the acceptable customer level. © 2024 IEEE.

关键词： Sales

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An Exploration of Interpolation - Machine Learning Model for Climate Model Downscaling Under the Limitation of data Quantity

An Exploration of Interpolation - Machine Learning Model for...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Prathom, Chotirose Champrasert, Paskorn Chiang Mai University Data Science Consortium Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai Thailand

ISBN: (纸本)9798350326413

In order to response to climate change, climate model is essential for future prediction, which is advantage for adaptation planning. With the fact that the output of the climate model is in a coarse spatial resolution, it is poorly representing data on local level and requires a downscaling process to increase its resolution. To perform the downscaling, high amount of historical observed data is necessary for calibrating the model output. However, the data may not be available in some area, also Thailand. To overcome the limitation of data Quantity, four combination methods between two interpolation (Inverse Distance Weight and Triangular Irregular Network) and two machine learning models (Artificial Neural Network and Gradient Boosting Regression Tree) are experimented to explore the proper one for downscaling data on Thailand area. By considering the performance of both interpolation and machine learning model, the combination between Inverse Distance Weight and Artificial Neural Network shows the best performance in down scaling process under data Quantity limitation. © 2023 IEEE.

关键词： Climate models

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Innovative Deep Learning Techniques for Solving Complex computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Securing 3D Point and Mesh Fog data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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An analysis of Network Security Attacks and their mitigation for Cognitive Radio Communication 9

An analysis of Network Security Attacks and their mitigation...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Gayathri, N. Anandakumar, H. Gowri, S. Keerthika, J. Nithyapriya, S. Sri Eshwar College of Engineering Computer Science and Engineering Tamilnadu Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350397376

The primary motivator after the evolution of cognitive radio is the lack of utilization of the spectrum. This technology adds new functionality in the physical layer, data link sub-layer, Transmission Control Protocol/Internet Protocol layer, and Transport layers of the hierarchy of software layers. The majority of the currently available research in cognitive radio has concentrated on security challenges at the physical, MAC layers. Over the past two decades, cyber security threats and problems have grown significantly, especially in cognitive radio networks. This study describes and analyzes various attacks that target each layer at its protocol stack. Each attack's specific detection techniques and defenses are also examined and contrasted. © 2023 IEEE.

关键词： Cognitive radio

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A Global Geometric Analysis of Maximal Coding Rate Reduction 41

A Global Geometric Analysis of Maximal Coding Rate Reduction

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Peng Liu, Huikang Pai, Druv Yu, Yaodong Zhu, Zhihui Qu, Qing Ma, Yi Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China Department of Electrical Engineering and Computer Science University of California Berkeley United States Department of Computer Science and Engineering The Ohio State University Columbus United States Institute of Data Science University of Hong Kong Hong Kong

The maximal coding rate reduction (MCR2) objective for learning structured and compact deep representations is drawing increasing attention, especially after its recent usage in the derivation of fully explainable and highly effective deep network architectures. However, it lacks a complete theoretical justification: only the properties of its global optima are known, and its global landscape has not been studied. In this work, we give a complete characterization of the properties of all its local and global optima, as well as other types of critical points. Specifically, we show that each (local or global) maximizer of the MCR2 problem corresponds to a low-dimensional, discriminative, and diverse representation, and furthermore, each critical point of the objective is either a local maximizer or a strict saddle point. Such a favorable landscape makes MCR2 a natural choice of objective for learning diverse and discriminative representations via first-order optimization methods. To validate our theoretical findings, we conduct extensive experiments on both synthetic and real data sets. Copyright 2024 by the author(s)

关键词： Network architecture

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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