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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4841-4850 订阅

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Scalable and Privacy-Severity Analysis of Pomegranate Leaf Diseases: Federated learning with CNNs 14

Scalable and Privacy-Severity Analysis of Pomegranate Leaf D...

引用

14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mehta, Shiva Kukreja, Vinay Vats, Satvik Manwal, Manika Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350335095

Pomegranate leaf ailments represent a danger to the production and yield of this widely produced fruit. Convolutional neural networks (CNNs), a machine learning methodology that combines computer vision techniques, have shown promise in identifying and classifying various disorders. However, the difficulty of data privacy and ownership often prevents these models from being trained effectively. This paper suggests using CNNs as part of a federated learning technique to overcome this obstacle and maintain the privacy of the training data. According to the approach, five client datasets representing various degrees of pomegranate leaf disease severity were used to train local models. The parameters of the local models were then combined into a global model. A comparison of the local customers showed impressive precision, recall, F1-Scores, and accuracy rates, ranging from 93.74% to 97.71%, demonstrating the model's effectiveness in identifying the disease's development in various regions. With precision rates ranging from 93.74% to 96.32% and equivalent rates for recall, F1-Score, and accuracy, the global model, created by weighting the local models, likewise showed strong performance across all customers. Macro, weighted, and micro averages were used to further analyse the performance of the global model, with findings ranging from 93.79% to 96.52%. These results support the model's balanced performance across various severity levels and its capacity to address class inequality. As a result, the work demonstrates the possibility of a federated learning strategy using CNNs to provide a reliable, scalable, and privacy-preserving method for identifying pomegranate leaf diseases. The results widen the use of federated learning in other fields where data privacy is crucial and provide opportunities for improving agricultural practices. © 2023 IEEE.

关键词： Convolutional neural networks

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A Machine learning Prediction Model for Envisaging Future Risk of Suicide 5

A Machine Learning Prediction Model for Envisaging Future Ri...

引用

5th Biennial international conference on Nascent Technologies in engineering, ICNTE 2023

作者： Nikam, Omkar Singh, Shreyash Patil, Swaraj Nair, Suryakrishnan Raut, Aniket Lobo, Vivian Brian More, Shraddha Laban, Ronald Melwin St. John College of Engineering and Management Department of Computer Engineering Palghar India St. John College of Engineering and Management Department of Information Technology Palghar India St. John College of Engineering and Management Department of and Telecommunication Palghar India

ISBN: (纸本)9781665465045

Suicides are a serious matter in the modern society. In India, suicide ideation/attempt is noticed to be one of the most common trends amongst youth. With each succeeding year, suicide rates are increasing in India. The southern and eastern states of India exhibit trends of high suicide rates. Current methods of suicide ideation/attempt detection include clinical visits, online counseling, etc. This study aims to propose an automated system that focuses on envisaging suicide rates of vivid states of India. Moreover, the proposed system forecasts an individual's risk of suicide with the help of a questionnaire filled and previously observed parameters. Machine learning techniques such as linear regression and decision-Trees are used, which achieved 85.98% and 90.476% accuracy, respectively. © 2023 IEEE.

关键词： Decision trees

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Unveiling the Anatomy of Phishing Attacks: Strategies, Targets, and Cybersecurity Solutions

Unveiling the Anatomy of Phishing Attacks: Strategies, Targe...

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2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Akshay Singh, Jaspreet Chandigarh University Computer Science and Engineering Punjab India

ISBN: (纸本)9798350313413

This paper undertakes a comprehensive analysis of phishing attacks, a pervasive and sophisticated cyber threat facing individuals, organizations, and online systems. It examines the techniques employed by attackers, including deceptive emails, website spoofing, and social engineering tactics. Additionally, emerging trends such as spear phishing, clone phishing, and voice-based phishing are investigated to draw attention to the constantly shifting landscape of malicious campaigns. To mitigate the risks associated with these attacks, existing countermeasures are critically evaluated for their strengths and weaknesses. These include both technical solutions like email filters and anti-phishing tools as well as user awareness programs that can enhance human resilience against phishing attempts. The findings of this research illustrate the importance of a multi-faceted approach in order to effectively combat phishing attacks and minimize the impact on individuals and organizations. © 2023 IEEE.

关键词： Countermeasures Cyber Threats Email Spoofing Machine learning Phishing Attacks Social engineering

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Research on emotional interaction and user cognition 4

Research on emotional interaction and user cognition

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Ying, Weiqiang Zhang, Lingyan Huang, Lanqing Jian, Luoshi Yao, Chen Ying, Fangtian College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310058 China Faculty of Humanities Macau University of Science and Technology 999078 China

ISBN: (纸本)9781510671232

This paper focuses on the field of emotional interaction, specifically using text data to evaluate expressed emotions. It aims to assess the emotional state of humans in order to determine how robots should adjust their behavior. To detect emotions in text data, various methods and algorithms can be utilized. In this study, the authors combine dictionary methods with machine learning algorithms for emotion detection. However, emotion labels are often vague and subjective, leading to a multi-label problem. To address this, the paper conducts experiments using naive Bayes, support vector machine, and neural network classifiers. Although the accuracy of emotion classification is low, the study demonstrates the importance of specific words in expressing emotions. © 2023 SPIE.

关键词： Support vector machines

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Web Based Book Recommendation System Using Collaborative Filtering 5

Web Based Book Recommendation System Using Collaborative Fil...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Mankar, Ketaki Pawar, Shruti Agarwal, Harsh Sangale, Tejas Kulkarni, Smita Mit Academy of Engineering Department of Computer Science Engineering Pune India Mit Academy of Engineering Department of Electronics and Electrical Engineering Pune India

ISBN: (纸本)9781665475242

Recommender systems are tools that help end users recommend products and obtain information about their preferences by going online. Today's online bookstores compete with each other in a variety of ways. One of the most powerful ways to efficiently increase revenue by attracting customers is a referral system. This study offers a clear, understandable method for recommending books that aids readers in selecting the best book. The proposed methodology works on training of database and feedback to provide meaningful information that helps users make decisions. In this paper recommendation system is developed by using collaborative filtering method. The machine learning (ML) model KNN is proposed to categorize the books as per user preferences. The overall architecture of the proposed system is introduced and its implementation is demonstrated. © 2023 IEEE.

关键词： Recommender systems

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Reinforcement-learning-Based Scheduling for Age of Information Optimization in MEC Multi-User Network 9

Reinforcement-Learning-Based Scheduling for Age of Informati...

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9th international conference on computer and Communications, ICCC 2023

作者： Gao, Ke Chen, Yuchao Chen, Guozhi Wang, Jintao Beijing China Tsinghua University Department of Electronic Engineering Beijing China Research Institute of Tsinghua University in Shenzhen Key Laboratory of Digital Tv System of Guangdong Province and Shenzhen City Shenzhen China

ISBN: (纸本)9798350317251

In this paper, we consider an optimization problem in a mobile edge computing (MEC) multi-user network for collecting fresh information under bandwidth and energy constraints. We use the popular metric Age of Information (AoI) to measure the data freshness. To solve the age optimization problem, we first relax the bandwidth constraints, and then decouple the problem, where each sub-problem is formulated into a constrained Markov decision process (CMDP) problem. Due to the fast mobility of the users, the distribution of channel state information and size of raw data is difficult to obtain for the base station. To overcome the unknown channel statistics challenge, we introduce a Reinforcement-learning-based Q-learning algorithm to learn the channel information, which helps to guide the optimal scheduling decision. Simulation results demonstrate that the proposed algorithm can achieve the better AoI performance compared with other traditional algorithm while satisfying bandwidth and energy constraints. © 2023 IEEE.

关键词： Reinforcement learning

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Review on Early Prediction of Body Dysmorphic Disorder Using Machine learning 2

Review on Early Prediction of Body Dysmorphic Disorder Using...

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Saini, Harnit Prasad, Sanjeev Kumar School of Computing Science of Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798350305418

Body Dysmorphic Disorder (BDD) is a psychiatric condition characterized by excessive preoccupation with perceived defects in one's appearance, often leading to distress and functional impairment. Early prediction of BDD is crucial for timely intervention and improved treatment outcomes. In recent years, machine learning techniques have shown promise in identifying individuals at risk of developing BDD based on various data sources, including facial images, clinical information, and textual data. This review aims to provide an overview of the current state of research on the early prediction of BDD using machine learning, highlighting the methodologies, challenges, and future directions in this emerging field. We conducted a comprehensive literature search and identified relevant studies that employed machine learning algorithms for BDD prediction. The reviewed studies demonstrated the effectiveness of machine learning techniques such as support vector machines, deep learning models, ensemble learning, natural language processing, feature selection and extraction, and clustering in predicting BDD. Despite the promising results, several challenges need to be addressed, including the availability of large and diverse datasets, standardization of evaluation metrics, and interpretability of the models. Future research should focus on refining the existing approaches, integrating multiple data modalities, and developing user-friendly tools for clinical practice. Early prediction of BDD using machine learning holds great potential for improving the identification and intervention of individuals at risk, ultimately leading to better outcomes and quality of life for individuals with BDD. © 2023 IEEE.

关键词： Body Dysmorphic Disorder clustering deep learning early prediction ensemble learning feature selection machine learning natural language processing support vector machines

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Adult Learners' Online Engagement and Perceived Outcomes with Educational Key Opinion Leaders: a Two-Phase Study 26

Adult Learners' Online Engagement and Perceived Outcomes wit...

引用

26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Xiaochen Shen, Jun Yan, Jun University of Wollongong Faculty of Engineering and Information Sciences Wollongong Australia

ISBN: (纸本)9798350331684

In recent years, especially during COVID, an increasing number of adult learners are flocking to e-learning platforms or mobile Apps for either personal or professional development. This research is built upon the constructivism model to investigate adult learners' satisfaction, perceived learning outcome, and recommendations towards Educational Key Opinion Leaders (Edu-KOLs). A two-phase study was designed specifically for this purpose, firstly using an online questionnaire distributed via the WeChat Platform to 203 per-qualified adult learners in China, followed by a one-on-one interview with selected ten. We adopted the quantitative research approach using partial least squares structural equation modelling (PLS-SEM) to interpret the collected data. The findings revealed that Edu-KOLs' knowledge level and course content have a significant influence on learners' perceived learning outcomes and customer advocacy. It was also indicated that higher engagement and interaction levels are favourably associated with their advocacy for the course and review for future potential learners. © 2023 IEEE.

关键词： E-learning

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SundariNet: A Multi-Scale Deep learning Approach for Sunderban Mangrove Ecosystem Landcover Classification 9

SundariNet: A Multi-Scale Deep Learning Approach for Sunderb...

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9th IEEE international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Ebrahim, Sadiq Siraj Bhattacharjee, Aditya Banerjee, Sriparna Chaudhuri, Sheli Sinha Jadavpur University Electronics and Telecommunication Engineering Department Kolkata India

ISBN: (纸本)9798350319651

Over the past few years, there has been a substantial shift in the interest of computer vision experts towards the automated categorization of land types using satellite imagery. This surge in interest can be attributed to the practical importance of this application in real-world scenarios. Amidst the array of land cover types, mangroves have emerged as a distinctive ecosystem with profound implications. They play a pivotal role in climate regulation along coastlines and provide a habitat for a rich diversity of plant and animal life. In this endeavor, we introduce an innovative and computationally streamlined deep learning architecture named 'SundariNet.' Crafted specifically for the effective classification of diverse land covers in the Sunderban region of West Bengal, India, SundariNet capitalizes on its exceptional capability to extract contextual features across multiple scales. This empowers it to simultaneously categorize numerous pixels within a patch, taking into account the intricate relationships between neighboring pixels. As a result, the architecture's performance efficiency is greatly amplified. Remarkably, SundariNet has achieved a classification accuracy of 99.64%, surpassing the benchmarks set by existing state-of-the-art methodologies. Our study harnesses freely accessible dual-polarized Sentinel-1 data from the selected Region of Interest (RoI), shedding light on the crucial role of intensity and phase values within each polarized channel for accurate classification. Detailed insights into these findings are presented in the Results section, underscoring the relevance and implications of our work. © 2023 IEEE.

关键词： Pixels

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Applications of Machine learning Techniques in the Classification of Mushrooms

Applications of Machine Learning Techniques in the Classific...

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2023 international conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Singh, Bhumi Dhanda, Namrata Verma, Rajat Amity University Uttar Pradesh Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350302820

Millions of different species of mushrooms are thought to exist today. Two main categories of mushrooms exist namely eatable and hazardous mushrooms. Since many individuals are unaware that mushrooms are harmful, they frequently contract food poisoning. There have been incidences of poisoning from poisonous mushrooms recorded even in some other nations. However, due to the abundance of mushrooms and their comparison-based qualities, it might be difficult to categorize them among edible and dangerous mushrooms. For the identification of the variety of mushrooms, precise classification helps to discover crucial forms, from many numbers of records of data. The technique used to categorize the categories of mushrooms using logistic regression, linear regression, support vector classifier, k-nearest neighbors, random forest, and gradient boosting. Among all the methods the random forest shows the greatest accuracy rate. © 2023 IEEE.

关键词： Nearest neighbor search

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