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Citrus Leaf Disease Classification Using Different Deep Learning Models: A Survey of Southern part of Bangladesh

Citrus Leaf Disease Classification Using Different Deep Lear...

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International Conference on computer and information Technology (ICCIT)

作者： Md. Masud Arpita Chokroborty Md. Masudur Rahman Partho Sarathi Sarker MD. Jubayar Alam Rafi Mirza Raquib Dept. of Computer Science and Engineering University of Global Village Barishal Bangladesh Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Citrus plant cultivation is becoming increasingly popular in South Asia, more specifically in India and Bangladesh region due to its rapidly growing demand as well as suitable weather conditions for farming. It can be affected by several diseases and requires keen attention to detect and cure the diseases in time; otherwise, significant monetary loss is incurred. With the advancement of computer vision and deep learning techniques, identifying various diseases is becoming simpler. However, this process requires a proper dataset of infected leaves and a suitable detector to recognize the diseases, and the existing renowned citrus disease datasets are based on a particular zone that may not be like the actual data that apply to the Barishal region. Therefore, a new dataset is proposed by doing two-step surveying in the southern part of Bangladesh that comprises images of infected citrus leaves with multiple classes of diseases i.e. 5 types of diseases, including precise annotations. Our subsequent investigation with these datasets consists of selecting basic CNN models for classification as well as applying the dataset to existing deep learning models such as VGG16, Xception, and InceptionV3 for performance and computational analysis. The high performance of those models proves the potentiality of the dataset. The dataset's diversity, clear images, transparent backgrounds, and real-life weather conditions led to the models achieving remarkable accuracy.

关键词： Deep learning Surveys computer vision Analytical models Adaptation models Accuracy Computational modeling information technology Diseases Meteorology

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability

Improving Time Series Forecasting Accuracy with Transformers...

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Electrical, Electronics, information and Communication Technologies (ICEEICT), International Conference on

作者： Susmera Joseph Ashly Ann Jo Ebin Deni Raj Dept. of Computer Science Engineering Indian Institute of Information Technology Kottayam India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder’s importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods.

关键词： Analytical models Accuracy Perturbation methods Time series analysis Refining Predictive models Transformers Stability analysis Thin film transistors Forecasting

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Designing an Application to Assist Wheelchair Users in Sports Activities through Image Recognition and Interactive Feedback

Designing an Application to Assist Wheelchair Users in Sport...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Pei-Jung Lin Jen-Wei Chang Kai-Yu Lin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

According to the World Health Organization, there are over one billion individuals globally relying on wheelchairs for daily activities, with this number continuously rising. Prolonged dependence on upper body movement predisposes individuals to risks such as muscle fatigue, shoulder pain, posture issues, and potential muscle imbalances. This study addresses the health needs of wheelchair users by designing a sports system named standpoint, WEAPP analyzes the limb conditions of wheelchair users, ensuring that shoulder pressure remains within acceptable limits to avoid excessive burden on their bodies while providing effective exercise. WEAPP is not only designed specifically for wheelchair users but also incorporates social features, enabling users to exercise together with friends. WEAPP aims to enhance the health status of wheelchair users, thereby increasing their sense of well-being and self-esteem.

关键词： Image recognition Pain Wheelchairs Interactive systems Organizations Medical services Muscles

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Texas A&m University - Central Texas Dept. of Computer Information Systems TX United States College of Business University of Tampa FL United States Embry-Riddle Aeronautical University Dept. of Electrical Engineering and Computer Science FL United States Bandirma Onyedi Eylül University Department of Software Engineering Bandirma Turkey

ISBN: (纸本)9798350362480

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls - such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture - this approach strengthens an organization's defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth's transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, e

关键词： Smart contract

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment

Enhanced Machine Learning Approach for Detecting Spammers in...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： K. Sai Bhavana B. Abhinandu P. Sai Sriyesh K. Rama S. Satheesh Kumar Dept. Computer Science & Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities.

关键词： Cloud computing Machine learning algorithms Clustering algorithms Production Machine learning Intelligent systems Hypertext systems Principal component analysis Industrial Internet of Things Graphical user interfaces

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Feature-based Intrusion Detection System with Support Vector Machine 1

Feature-based Intrusion Detection System with Support Vector...

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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Khodaskar, Manish Medhane, Darshan Ingle, Rajesh Buchade, Amar Khodaskar, Anuja Pune Institute of Computer Technology Dept. of Information Technology Pune India MVPS's K.B.T. College of Engineering Dept. of Computer Engineering Nashik India Dept. of Computer Science & Engineering Naya Raipur India Mit School of Engineering Mit Art Design and Technology University Department of Computer Science & Engineering Pune India Sipna College of Engineering and Technology Dept. of Computer Engineering Amaravati India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS. © 2022 IEEE.

关键词： Support vector machines

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A Comprehensive Review of Routing Protocols in Vehicular Ad Hoc Networks (VANETs): Challenges, Solutions and Future Directions

A Comprehensive Review of Routing Protocols in Vehicular Ad ...

引用

IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Sonika Bhardwaj Ramesh Saha Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

VANETs play a pivotal role in the growth/development of the intelligent transportation systems (ITS) by allowing seamless communication between vehicles and infrastructure. This paper presents a comprehensive review of various routing protocols used in VANETs, focusing on topology-based, position-based, hybrid, and machine learning (ML) enhanced protocols. The analysis evaluates these protocols across key performance metrics such as scalability, latency, packet delivery ratio (PDR), overhead, and security. Hybrid and context-aware protocols provide greater adaptability but come with more complexity than standard protocols, which struggle with scalability and security in dynamic contexts. The growing combination of ML, blockchain, and federated learning into VANET routing establishes potential for enhanced performance and security. Furthermore, the paper also highlights the current limitations of an existing protocols and recognizes the directions for the research, the more emphasizing on the need for scalable, secure, and real-time adaptable solutions for next-generation VANETs.

关键词： Federated learning Reviews Scalability Vehicular ad hoc networks Routing Routing protocols Real-time systems Blockchains Security Vehicle dynamics

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Code Smell Detection using Hybrid Machine Learning Algorithms 5

Code Smell Detection using Hybrid Machine Learning Algorithm...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Mahalakshmi, D. Kasinathan, Prabakaran Elangovan, D. Rohith Bhat, C. Balamurugan, M. Sivakumar, S. Panimalar Engineering College Dept. of Information Technology India School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Dept. of Cse India Panimalar Engineering College Dept. of Cse India Chennai India Sri Sairam Engineering College Affiliated to Anna University Dept. of Cse India Lakshmi Bangaru Arts and Science College Dept. of Computer Applications melmaruvathur India

ISBN: (纸本)9798350321425

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods. © 2023 IEEE.

关键词： Learning systems

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Generating High-Quality Distributed Representations of Words Using Wiktionary and Sentence-BERT

Generating High-Quality Distributed Representations of Words...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Ryota Nishiura Seiji Tsuchiya Hirokazu Watabe Dept. of Information and Computer Science Graduate School of Science and Engineering Doshisha University Kyotanabe Kyoto Japan Dept. of Intelligent Information Engineering and Science Faculty of Science and Engineering Doshisha University Kyotanabe Kyoto Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

We propose an improved method to generate high-quality distributed representations of words. In our previous paper, we introduced a novel method to obtain distributed representations of words with BERT using Wiktionary as semantic sources of words. The previous method outperformed a Wrd2Vec model in a word similarity task, while there still had to be some room to improve in term of semantic accuracy. In this paper, our new method introduces one of Sentence-BERT models to text-embed the semantic sources instead of the original BERT model. We demonstrate that our proposed method outperforms both our previous method and the Word2Vec model in the tasks.

关键词： Vocabulary Accuracy Semantics Encyclopedias Internet Online services Informatics

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

关键词： Adversarial machine learning

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