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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Dani, D.S. Eunice Little Rohini, K. Department of Computer Science Vels Institute of Science Technology & Advanced Studies Tamilnadu Chennai India School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Tamilnadu Chennai India

ISBN: (纸本)9798350374933

The rapid expansion of geospatial data analysis has led to an increasing need for systems that can respond quickly. Storing and analyzing extensive spatiotemporal datasets is crucial for extracting valuable information for location-aware applications like route planning and weather forecasts. Efficiently coordinating data sources and internet services is crucial for extracting results within time constraints, given the current surge in processing geographic searches. Privacy models that incorporate fog computing provide a significant advantage in terms of cost reduction and low latency. However, in order to address consumers' concerns about data exposure, recent efforts have been made to integrate fog computing for enhanced privacy. Through the use of simulated outcomes, it has been shown that the privacy protection offered by the Geo-Fog framework for localization services is highly effective. The system demonstrated average latency values as low as 23 ms for query loads of 200. Furthermore, the CPU usage remained below 37% even under the maximum query load of 500. Fog computing plays a crucial role in the advancement of location-based applications that prioritize efficiency and privacy. By enabling fog devices or servers to handle geospatial queries within the geographic environment, it ensures optimal management. © 2024 IEEE.

关键词： fog computing Geospatial data location privacy location-based services privacy protection query resolution

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Early-Stage Disease Prediction in Chilli Plant Using YOLO Models 2

Early-Stage Disease Prediction in Chilli Plant Using YOLO Mo...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Manoj, R. Girish, Sandra Pushpa, B.R. Rani, N. Shobha Sangamesha, D. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Chemistry The National Institute of Engineering Mysore India

ISBN: (纸本)9798350383867

Agriculture is an important component of every country's economy, supplying the necessary resources to farmers and their families. The livelihoods of farmers are greatly threatened by crop diseases, which highlights the importance of early identification of diseases in chilli plants at the right stage of growth is crucial for timely fertilizer recommendation. This paper presents an intelligent transfer learning technique to detect thrips and viruses in chilli plants in the early stages of development. Stage-by-stage datasets are collected from chilli plant orchards around Mysore district, Karnataka, to carry out the training using the YOLO model. The dataset consists of three growth stages of chilli plants (15, 25, and 35 days) that are captured in diverse environmental settings with multiple-resolution smart phones. The annotations are created for the diseases healthy, viruses, and thrips at leaf level from whole plants for all three stages. Hyperparameters such as learning rate, the learning optimization algorithm, batch size, epochs, and loss functions are employed to fine-tune the YOLO v5, v6, and v7 models. To achieve high generalisation and reduce overfitting, a weight decay (weight_decay) of 0.0005 is employed. From the evaluation, it was noticed that YOLOv7 outperformed other YOLO models. A mAP score of 0.80 is achieved in stage 1, 0.82 in stage 2, and 0.73 in stage 3 with minimal validation loss and remarkable inference speed. © 2024 IEEE.

关键词： Plant diseases

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Analysis of Water Quality in Cauvery River by Using PCA with Various Classifications Technique 3

Analysis of Water Quality in Cauvery River by Using PCA with...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Karpagalakshmi, R.C. Selvam, Prabhu Sakthivel, S. Kalaivanan, K. Sharma, Rajesh Sungheetha, Akey Mahapatra, Sheila Department of Computer Science and Engineering Alliance University Bangalore India School of Computing SRM Institute of Science and Technology Tiruchirappalli India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Water quality assessment is a complex endeavour that involves identifying pollutants in water resources. The importance of this process lies in its objective to evaluate water quality for human use. Machines and deep learning algorithms play a vital role in this evaluation. In this context, feature selection methods were employed to identify critical factors that ensure optimal accuracy. Subsequently, these selected features were used as input for various classifiers for classification purposes. The Cauvery River dataset, obtained from the Tamil Nadu Pollution Control Board, was utilized to assess the performance of the proposed approach. This research was implemented using Python as the programming language. The performance of the PCA-RF model was evaluated using various metrics, including an accuracy of 0.96, precision of 0.97, recall of 0.94, and an F1-score of 0.95. The results demonstrate that the PCA-RF model outperforms conventional machine learning approaches, achieving a high R-squared score of 0.95. © 2025 The Author(s).

关键词： River pollution

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

Language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Cloud computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems 4

Enhancing Cloud Computing Security through Deep Learning and...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shukla, Sunil Singh, Jagendra Ramya, T. Rahul, Satyakam Mallick, Arnab Kumar Pandey, Pawan Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Mit Adt University School of Computer Science Engineering Pune India Indian Institute of Technology Madras Department of Computer Science Engineering Chennai India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350349900

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of 94.4%, with a false positive rate of 2.2% and a computational efficiency of 1 5 8. 4 milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of 96.2%, a false positive rate of 1.1%, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud. © 2024 IEEE.

关键词： Cloud security

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NTFS: Introduction and Analysis from Forensics Point of View 2

NTFS: Introduction and Analysis from Forensics Point of View

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology School of Computer Engineering and Mathematical Sciences Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

New technology File System (NTFS) is a file system being used by Windows Operating Systems since 1993. NTFS is a file system used by the Windows Operating Systems for storing, cataloguing and discovering/finding files. In this work we will learn about the basics of NTFS to include its file structure and other important attributes. Then carry out analysis of NTFS file system using WinHex and Active@ Disk Editor tools from a forensics point of view, compare the results of both tools. Lastly, we carried out comparison of NTFS in Windows 10 and 11 Operating System to include forensics analysis using WinHex and Active@ Disk Editor tools. © 2023 IEEE.

关键词： Windows operating system

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End-to-End Optimized Lossy Compression for Neural-Morphic Spiking Camera Captured Data

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6074-6086页

作者： Feng, Kexiang Jia, Chuanmin Pan, Jingshan Ma, Siwei Gao, Wen Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University National Engineering Research Center of Visual Technology Beijing100871 China Peking University Wangxuan Institute of Computer Technology Beijing100080 China Shandong Computer Science Center National Supercomputer in Jinan Jinan250014 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Chinese Academy of Sciences Beijing100190 China

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression. © 1991-2012 IEEE.

关键词： Image compression

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Skin Cancer Classification using A Hybrid Convolutional Neural Network with SVM Classifier

Skin Cancer Classification using A Hybrid Convolutional Neur...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Chatrapathy, K. Prasad, M Lakshmi Kiran, Ajmeera Shaker Reddy, Pundru Chandra Babu, G Charles Partheeban, N. Reva University School of Computing & Information Technology Karnataka Bengaluru India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Mlr Institute of Technology Computer Science and Engineering Telangana Dundigal Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India Galgotias University School of Computing Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350308167

Digital dermoscopy is commonly employed by dermatologists for the diagnosis of malignancy. Clinicians' recognition of tumor is not only highly subjective, but also highly dependent on their level of experience. The inherent inter-operator unevenness in the private processing of dermoscopy pictures calls for fully automated computer-aided diagnosis(CAD) methods. The fine-grained differences in the manifestation of these abrasions on the skin-surface make automated skin lesion classification difficult. In several different types of fine-grained object classifications, deep convolutional-neural-network(CNN) has demonstrated excellent reparability. In order to categorize dermoscopy pictures into benign or malignancy lesions, this research introduces two innovative hybrid CNN strategies that use a support-vector-machine (SVM) at the output layer. In order to classify data, the characteristics mined by the first &second CNN strategies are combined and supplied to the SVM. The suggested model's efficacy is measured against a reference set of labels provided from a dermatology specialist. In a study using CAD for skin disease categorization, the generated CNN-segmented image folder distinguished atopic dermatitis from nasty diseases and obstacles like mycosis fungoides, impetigo, and herpesvirus contagion with a sensitivity and specificity of around 98%. In comparison to the traditional CNN strategies, the proposed models performed better on the ISBI 2016 public dataset. Both of the proposed models, with an accuracy of 88.02% and 87.43%, outperformed the standard CNN models. Reduced reliance on physician annotation and enhanced CAD performance are both benefits of our CNN segmentation algorithm, which automatically removes lesions and segments images of the skin area across all image fields. © 2023 IEEE.

关键词： CAD Convolutional-neural-network deep learning dermoscopy Skin cancer SVM

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