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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Anu Priya, A. Rajaram, K. Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Brinjal leaf diseases, such as leaf spot, mosaic virus, and wilt, would critically reduce crop yield quality if the disease is spread during an early stage. Such reasons have made early identification imperative to ensure effective prevention and management. This article, therefore, aims at coming up with an effective technique of identifying brinjal leaf diseases using VGG16 CNN model. Traditional identification methods are based on manually examining plant diseases, an effort known to be highly labour-consuming, subjective, and even unreliable. In addition, most of the available machine learning methods fail in scalability, computational cost, and specific domain problems. However, our technique makes use of the high ability of VGG16 architecture to extract automatically relevant features from leaf images and then classify them with high accuracy. Used Eggplant Leaf Disease Dataset which contains six classes. The central idea of our approach is to employ deep convolutional layers of VGG16 to extract discriminative features from the images followed by classification using fully connected layers and softmax activation. The approach obtains 98.2% accuracy for determining of Brinjalleaf diseases. This work will impact the agricultural sector by means of providing a fast automated solution that gives accurate insights about early detection of leaf disease occurrence, hence reducing losses to crops and enhancing production. © 2025 IEEE.

关键词： Convolutional neural networks

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A Delay Sensitive Framework for Effective Healthcare using Machine Learning 10

A Delay Sensitive Framework for Effective Healthcare using M...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper proposes a delay-sensitive framework for the Healthcare Industry that can effectively predict diseases at an early stage. The framework utilizes Machine Learning (ML) techniques to integrate the Internet of Things (IoT), Cloud Computing, and ML in the medical field. The primary objective is to reduce the delay on the user's end while using Smart Healthcare services and provide the quickest response to the patient. The proposed framework can contribute to the better and optimal integration of emerging technologies in the Healthcare sector. The methodology involves collecting data from various sources, using ML algorithms, preprocessing feature extraction, and model training. The major findings indicate that the proposed framework can effectively predict diseases early and improve the quality of healthcare services. The interpretations and implications of the study highlight the importance of integrating emerging technologies in the Healthcare sector to provide better healthcare services to patients. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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A Novel Framework for Prediction of Heart Disease using Hybrid Classifier in the Cloud Environment 10

A Novel Framework for Prediction of Heart Disease using Hybr...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Prerna Singh, Prabhdeep Singh, Devesh Pratap Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

This paper presents a novel framework for predicting heart diseases in the cloud environment by employing hybrid classifiers. The dataset used in this research is the Cleveland heart disease dataset. Four hybrid classifiers were tested: Naive Bayes + Random Forest, Bayes Net + Random Forest, Naive Bayes + J48, and Naive Bayes + PART. The performance of the hybrid classifiers is evaluated by comparing accuracy, precision, recall, F. Measure, and PRC area. The results showed that the accuracy of the hybrid classifiers is significantly improved compared to the individual classifiers. The Naive Bayes + Random Forest hybrid classifier achieved the best results with the highest accuracy, F. Measure, and PRC area among the four hybrid classifiers. The proposed framework provides a useful approach to predicting heart disease in the cloud environment. This framework could diagnose heart diseases more accurately and improve healthcare services. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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A Specular Reflection Removal Technique in Cervigrams 1

A Specular Reflection Removal Technique in Cervigrams

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Mukku, Lalasa Thomas, Jyothi Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcopes. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several prominent approaches for removal of specular reflections proposes a novel method to remove the specular reflections. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. © 2023 IEEE.

关键词： Diseases

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Harnessing the Combined Power of Artificial Intelligence and Machine Learning for Diagnosis of Brain Tumor 5th

Harnessing the Combined Power of Artificial Intelligence and...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Dholaria, Jay Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The process of diagnosing diseases stands as a pivotal conduit for converting observed clinical evidence into precise disease appellations. Brain cancer, characterized by the uncontrolled growth of abnormal cells within the brain or its associated structures, is a relentless and complex disease that continues to pose significant challenges in the field of oncology. This abstract provides a concise overview of ongoing research efforts aimed at advancing our understanding of brain cancer and developing innovative approaches for early detection and more effective therapeutic interventions. Focusing on the domain of Brain Cancer, the author explores the application of Machine Learning (ML), Artificial Intelligence (AI), and Soft Computing (SC) algorithms to enhance the detection and diagnosis of brain cancer. In conclusion, the objectives of these explorations transcend mere technical validation. Rather, they are intrinsically motivated by the quest to discern the pragmatic feasibility and clinical utility of these advanced algorithms. Amidst the uncertainty and complexity inherent to cancer diagnosis, these algorithms emerge as beacons of decision-making support. The crucible of risk is where they are evaluated, offering insights into the adaptability, robustness, and potential pitfalls associated with their application. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Diseases

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An IoMT data security framework with Hyperledger Fabric for smart cities

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4875-4886页

作者： Samanta, Saikat Sarkar, Achyuth Kumari, Sangeeta Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

There is a high-risk privacy issue related to the Internet of Medical Things (IoMT) because of a lack of security in critical and sensitive information. We propose a framework for securing medical data in Healthcare 5.0 generated by the IoMT. Our framework uses Hyperledger Fabric (HF), a permissioned blockchain platform, to provide a decentralized and tamper-proof system for data management and sharing. The proposed framework includes modules for identity management, data encryption, and access control for Healthcare 5.0. Hyperledger is a Linux Foundation-hosted open-source project. The HF is one of the IBM-developed initiatives that eventually contributed to Hyperledger. The paper presents the architecture of the framework, as well as a prototype implementation and evaluation of its performance and security specific to consumer electronics for Smart Cities. This evaluation demonstrated that the proposed framework is efficient and effective at securing medical data in the IoMT, and could be used to develop secure and scalable healthcare systems in the future. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain technology Decentralized data management Healthcare data privacy Remote patient monitoring Smart healthcare

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A Novel Approach for Sentiment Analysis on social media using BERT & ROBERTA Transformer-Based Models 8

A Novel Approach for Sentiment Analysis on social media usin...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Prasanthi, Kundeti Naga Eswari Madhavi, Rallabandi Sai Sabarinadh, Degala Naga Sravani, Battula Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350334012

Sentiment analysis on social media platforms has gained immense popularity in recent years due to its ability to analyze and classify users' emotions and opinions on different topics. This paper proposes a sentiment analysis model for Twitter that utilizes two powerful transformer models BERT and ROBERTA, to classify tweets based on their sentiment. The use of BERT and ROBERTA transformers offers several advantages over traditional sentiment analysis models. These transformers can capture contextual information and understand the relationships between words in a sentence, making them well-suited for the nuanced language used in social media platforms like Twitter. To train the model, transfer learning is employed, allowing the transformers to learn from a vast dataset of tweets before fine-tuning on the sentiment analysis task. This approach enables the model to understand the nuances of informal language and slang used in tweets, improving its ability to accurately classify sentiment. Another advantage of the proposed model is its ability to handle the informal language used in tweets. However, BERT and ROBERTA transformers have been trained on a vast corpus of informal language, enabling them to accurately analyze and classify sentiment in tweets. Lastly, the proposed model's scalability and efficiency are also advantages. This model can handle large volumes of data with high efficiency and can be fine-tuned to adapt to new domains with minimal training data, making it highly versatile and applicable in various applications. © 2023 IEEE.

关键词： Sentiment analysis

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Internet of Things Enabled Smart Hand Gesture Virtual Mouse System 14

Internet of Things Enabled Smart Hand Gesture Virtual Mouse ...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Chelekkodan, Danish Muneer Jayapandian, N. Abhinav, S. Shyjan, Aaron Department of Computer Science and Engineering Christ University Kengeri Campus Bangalore India

ISBN: (纸本)9798331531935

This research is aim to focus on IoT based hand gesture model. Mouse is one of the most important input devices of a computer. It works as a pointing device and allows the user to move the pointer as needed by the user. In the early days, a wired mechanical mouse was used for this purpose. In mechanical mouse a ball is fixed underneath the mouse, which rotates as the user moves the mouse. This movement of the ball is used to move the mouse pointer on the screen. Now mostly we use optical mouse which can be wired or wireless. An optical mouse has a high-power laser below it, which takes more than thousand pictures of the surface below the mouse. An image comparator compares the images and sends the signal to move the mouse pointer as the texture of the image changes. Both the types of mouse works based on old technology. As technology leaps to greater heights, the need for simplicity also increases. With the invention of different kind of sensors, microcontrollers and other electronics, we can eliminate the mouse as an input device and instead use our hands to do the work of a mouse. This prototype is an embedded system which runs with the help of an arduino microcontroller. Flex sensors are used to capture the hand gestures. The proposed IoT based hand gesture model is providing high accuracy rate compare to the regular model. The proposed model is analyzed with accuracy level, the average accuracy level of proposed model is more than 90%. © 2025 IEEE.

关键词： Comparators (optical)

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Identifying Texting Idiolects using Transformers 5

Identifying Texting Idiolects using Transformers

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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Bharath, T.U. Suresh, Subalesh Suresh, Kavya Shaji, Sikha Mathew, Tessy Department of Computer Science and Engineering Trivandrum India

ISBN: (纸本)9798350334128

Nowadays internet anonymity has turned into a head-scratcher issue for most law enforcement agencies. As the technological developments in the digital field are still rising exponentially so are cyber crimes. In cyber forensics, digital profiling of anonymous tweets is done by analysing and comparing the suspect's tweets texting idiolects the anonymous author's writing style. Hence our project proposal is to develop a model using transformers and natural language processing (NLP) techniques for identifying the author of the anonymous chat. © 2023 IEEE.

关键词： Long short-term memory

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Isolated word recognition based on a hyper-tuned cross-validated CNN-BiLSTM from Mel Frequency Cepstral Coefficients

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Multimedia Tools and Applications 2024年第17期84卷 1-20页

作者： Paul, Bachchu Phadikar, Santanu Bera, Somnath Dey, Tanushree Nandi, Utpal Department of Computer Science Vidyasagar University West Bengal Midnapore721102 India Department of Computer Science & amp Engineering Maulana Abul Kalam Azad University of Technology BF-142 Sector-I Salt Lake West Bengal Kolkata700064 India

Speech Recognition(SR) is a challenging task because existing models expect their users from different geographies to have the same level of proficiency, which is unrealistic. Existing models achieve high classification accuracy for low to mid-sized datasets but struggle with real-time data from non-native speakers. There hasn't been any development in deep learning-based cross-validation (CV) training for speech recognition yet. Here, a hyper-tuned Convolutional Neural Network (CNN) followed by a Bidirectional Long Short Term Memory (BiLSTM) network has been proposed to classify isolated words using CV training to address these issues. The CNN extracts the spatial relationship among the features, and the long-term dependency of the input sequence is predicted using the BiLSTM. For this sequence-to-sequence classification, the frame-wise 10, 14, 18, and 22 Mel Frequency Cepstral Coefficients (MFCC) features have been extracted first from each speech signal. Subsequently, a post-processing step is undertaken in which an imbalanced dataset is transformed into a balanced one, and the feature matrix is reshaped to a uniform format suitable for input into the proposed model. The experimental setup is arranged such that each sample is processed by the CNN-BiLSTM and the results are recorded in individual training histories. In the experimental study, we use two mid-volume standard datasets, "The Free Spoken Digit Dataset" (FSDD) and "The Arabic Speech Corpus for Isolated Words" (ASCIW) of two different languages. Applying the proposed technique, the average CV accuracy obtained is 99.81% and 99.75% for the FSDD and the ASCIW dataset respectively using 18 MFCC, which relatively outperforms the existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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