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Ensemble Learning for Enhanced Mango Leaf Disease Classification: A Deep Learning Approach 3

Ensemble Learning for Enhanced Mango Leaf Disease Classifica...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Neog, Protyush P. Kaur, Navjot Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331527549

Mangoes are highly consumed fruits especially in the tropical as well as subtropical zones of the earth. It is said that nearly half of the earth’s population consumes mangoes in some form or the other either as fresh mangoes or in processed form such as juice, dried mangos, or other cooked dishes. That said, this significant food crop has a number of limitations in its cultivation and the least of these is attack by pests. The agro-ecosystem of mango plantation includes several factors such as soil, insects, diseases, weather and many more which determine the well-being of the mango trees in that plantation. Of all these concerns, leaf diseases turn out to be the most offensive by causing losses in both the quantity and quality of mangoes produced. To mitigate these problems, research has been done and continues to be done towards identifying and classifying the mango leaf diseases with better yields and less losses thanks to processing techniques such as deep learning and image processing. Recently published papers focus on these modern methods and analyze the advantages of their applications for the diagnosis and treatment of mango plants. This paper proposes a novel framework that identifies the diseases in mango leaves. The approach demonstrates an impressive 99.94% testing accuracy employing ensemble learning and transfer learning techniques. It is superior to other popular deep learning-based models that include DenseNet121, ResNet50 and VGG19. The results of the study demonstrate the efficiency of the proposed approach as well as its superiority in important performance metrics such as precision and recall. This evolution provides a viable answer to the very pertinent challenge of disease detection in mango cultivation allowing for better management of crops and hence increased production. © 2025 IEEE.

关键词： Plant diseases

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： Big Data Chatbot Cost Data Mining e-Commerce Hadoop Machine Learning Product Purchasing Recommendation

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Ensuring Trust in Blockchain-Based E-commerce Using Smart Contracts 5th

Ensuring Trust in Blockchain-Based E-commerce Using Smart Co...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Surendar, P.D. Kumar, Adhithi Satish Pavani, Venkata Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (纸本)9789819773701

In the ever-changing realm of e-commerce, building trust is crucial for both customers and sellers. Reputation systems play a vital role in enabling informed decision-making by providing vendor ratings and reviews. Conventional reputation systems are typically inadequate in dealing with contemporary issues including privacy, identity theft, and feedback fraud. In order to address these challenges, we suggest implementing a decentralized reputation system that places a high value on privacy and effectively counters existing risks. Our architecture utilizes two permissioned blockchains, Hyperledger Indy for decentralized identity management through verifiable credentials and Zero Knowledge Proof (ZKP) technology, guaranteeing anonymity and authenticity, and Hyperledger Fabric for executing smart contracts, facilitating intricate business logic. The system incorporates verified vendor identities, transaction feedback tokens, and discount tokens to encourage genuine evaluations. Smart contracts implemented on the Hyperledger Fabric platform provide the security and transparency of the feedback process, hence reducing the occurrence of biased feedback and identity assaults. This technique provides a strong and reliable answer to the issues faced in e-commerce. It ensures the protection of private information and enables the verification of digital identities while maintaining privacy. The implementation of feedback and discount tokens promotes the provision of precise input, mitigating unjust practices and bolstering the dependability and confidence in the system. We demonstrate the effectiveness of our methodology using Zero Knowledge Proofs (ZKP), highlighting its importance in establishing confidence within the context of blockchain. Our system represents a notable advancement in creating a safe, transparent, and reliable e-commerce environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Comparative Assessment of Accessibility and Readability in Generative AI: A Case Study of GPT and Google BARD 4th

Comparative Assessment of Accessibility and Readability in G...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Afsal, C.P. Kuppusamy, K.S. Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9789819757022

The accessibility and readability of Generative Artificial Intelligence systems like GPT and Google BARD are crucial factors that require thorough examination. In today’s digitally connected world, where AI-generated content is becoming increasingly prevalent across various sectors, ensuring that such content is accessible to a broad audience, easy to understand, and user-friendly is essential. This research was motivated by the growing influence of AI-generated content on various user groups, including individuals with varying levels of familiarity with technology and diverse cognitive abilities. This paper comprehensively assesses and draws comparisons between the accessibility and readability of Google BARD and GPT to identify areas for enhancement. GPT exhibits various accessibility issues across different WCAG 2.0 conformance levels, contrasting with Google BARD, which shows no accessibility problems. The results underscore the importance of addressing accessibility concerns in AI models to ensure inclusivity and user-friendliness for individuals with disabilities. Google BARD consistently provides more readable content than GPT, underscoring the importance of selecting the appropriate tool based on the target audience and desired readability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ambient intelligence

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A Hybrid Deep Learning Approach for Early Detection of Diabetic Retinopathy 3

A Hybrid Deep Learning Approach for Early Detection of Diabe...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mary Vespa, M. Shajeena, J. Shiny, R.M. Gayathri Devi, K. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Diabetes is a malnutrition that results from elevated glucose levels. Diabetes eventually results in diabetic retinopathy (DR), a retinal condition that significantly impairs vision. It is an insulin-dependent difficulty that impacts ocular health. The receptive to cells at the back of the individual retina's veins gets injured, which is the cause of it. A favorable prognosis for this condition depends on its early identification. In this paper, the application of Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) on color fundus images is used for diabetic retinopathy is applied for understanding the task. The goal of the proposed DR identification method is to use deep learning to automatically identify the problem. An hybrid model that was trained and tested to distinguish between healthy and DR-affected fundi was able to achieve an accuracy of roughly 99.07% using fundus images from diabetic patients that were available in MESSIDOR. © 2025 IEEE.

关键词： Convolutional neural networks

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Escape Routing Using Deep Reinforcement Learning

Escape Routing Using Deep Reinforcement Learning

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Cheng, Hsu Fu Cheng, Dun Wei Department of Computer Science and Information Engineering National Yunlin University of Science & Technology Yunlin Taiwan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Escape routing is a critical task in the design of printed circuit boards (PCB) and integrated circuits (IC), aiming to establish effective connections among multiple pin points while avoiding path overlaps and interference from *** paper proposes a Deep Reinforcement Learning (DRL)-based solution utilizing a Deep Q-Network (DQN) to address the escape routing *** problem is modeled as a Markov Decision Process (MDP), and the agent learns effective routing strategies through interactions with the *** were conducted in a 25×26 simulated grid environment, testing scenarios with 30, 60, and 90 pin *** study highlights the potential of Deep Reinforcement Learning in solving escape routing problems, offering a novel approach to addressing routing challenges in PCB design. © 2024 SPIE.

关键词： Deep reinforcement learning

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A Multi Model Deep Learning Framework for Sarcasm Detection in Regional Social Media: An Overview 1

A Multi Model Deep Learning Framework for Sarcasm Detection ...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Bhadgale, Anilkumar M. Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language Processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Convolutional neural networks

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Classifying Enzymatic Functions Using Graph Neural Networks: Insights from the ENZYMES Dataset 22

Classifying Enzymatic Functions Using Graph Neural Networks:...

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22nd International Learning and Technology Conference, L and T 2025

作者： Attiah, Ameera Alagha, Mawaddah Sharab, Leen Effat University College of Engineering Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

This study explores the use of Graph Neural Networks (GNNs) to classify enzyme functions using the ENZYMES dataset, which represents proteins and their interactions as graphs. The literature review highlights the evolution and efficacy of Graph Neural Networks (GNNs) in capturing complex relational data in graph structures, revolutionizing fields such as bioinformatics by facilitating advanced enzyme classification and potential drug discovery through enhanced analysis of biological interactions. The methodology uses the ENZYMES dataset from the TUDataset, which encodes protein structures into graphs for enzyme function classification. A structured Graph Neural Network was utilized with layers designed to capture complex graph hierarchies, employing preprocessing steps, advanced graph convolution techniques, and rigorous training procedures to ensure the model's effectiveness and reproducibility in predicting enzyme functions. The study demonstrates that the Graph Neural Network model effectively learned and generalized enzyme function classification, as evidenced by consistent improvements in training performance and strong testing results. The model's robustness was further validated through detailed analyses such as confusion matrices, ROC curves, and precision-recall metrics, which also identified specific areas for future improvement in feature engineering and training approaches to enhance accuracy and model interpretability. © 2025 IEEE.

关键词： Graph neural networks

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Checking the Truthfulness of News Channels using NLP Techniques

Checking the Truthfulness of News Channels using NLP Techniq...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Vipin, C. Harshit, S. Sailesh Kumar, K.L. Vijay, K. Zaid, Mohamed Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306088

Data analytics are difficult with unstructured data since it lacks pre-formatting, which makes up a large amount of information on social media. Research has looked into the potential of machine learning and natural language processing to interpret unstructured data and uncover significant information that could have gone unnoticed. We classify social media users using segmentation approaches on the basis of population, relationship status, and other criteria, allowing for the recognition of prominent people within particular categories. Behavior analysis assigns distinct roles to social media participants, such as users, recommenders, prospective users, and detractors, facilitating the tailoring of messages and reactions to address their concerns effectively. The study involves the accumulation of data from three news channels, data cleaning, and exploratory data analysis (EDA) to unearth patterns. Sentiment analysis is performed, and its results are compared for each of the news channels. Topic modeling is used to reveal different themes in the data. This study offers a solid basis for sentiment analysis and visualization, providing insightful information on the sentiment levels across various source kinds and features. It holds the potential to empower government agencies and businesses in harnessing the power of social media analytics for informed decision-making and improved communication strategies. © 2023 IEEE.

关键词： Visualization

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Multi-Valued Connected Consensus: A New Perspective on Crusader Agreement and Adopt-Commit 27

Multi-Valued Connected Consensus: A New Perspective on Crusa...

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27th International Conference on Principles of Distributed Systems, OPODIS 2023

作者： Attiya, Hagit Welch, Jennifer L. Department of Computer Science Technion Haifa Israel Department of Computer Science and Engineering Texas A&M University College StationTX United States

ISBN: (纸本)9783959773089

Algorithms to solve fault-tolerant consensus in asynchronous systems often rely on primitives such as crusader agreement, adopt-commit, and graded broadcast, which provide weaker agreement properties than consensus. Although these primitives have a similar flavor, they have been defined and implemented separately in ad hoc ways. We propose a new problem called connected consensus that has as special cases crusader agreement, adopt-commit, and graded broadcast, and generalizes them to handle multi-valued inputs. The generalization is accomplished by relating the problem to approximate agreement on graphs. We present three algorithms for multi-valued connected consensus in asynchronous message-passing systems, one tolerating crash failures and two tolerating malicious (unauthenticated Byzantine) failures. We extend the definition of binding, a desirable property recently identified as supporting binary consensus algorithms that are correct against adaptive adversaries, to the multivalued input case and show that all our algorithms satisfy the property. Our crash-resilient algorithm has failure-resilience and time complexity that we show are optimal. When restricted to the case of binary inputs, the algorithm has improved time complexity over prior algorithms. Our two algorithms for malicious failures trade off failure resilience and time complexity. The first algorithm has time complexity that we prove is optimal but worse failure-resilience, while the second has failure-resilience that we prove is optimal but worse time complexity. When restricted to the case of binary inputs, the time complexity (as well as resilience) of the second algorithm matches that of prior algorithms. The contributions of the paper are first, a deeper insight into the connections between primitives commonly used to solve the fundamental problem of fault-tolerant consensus, and second, implementations of these primitives that can contribute to improved consensus algorithms. © Hagit Attiya and

关键词： Computational complexity

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