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

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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Automated recognition of Myanmar sign language using deep learning module

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International Journal of Information technology (Singapore) 2024年第2期16卷 633-640页

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Computer Science and Engineering GSFC University Vadodara Gujarat India

The recognition of sign language in real-time poses a significant challenge due to the dynamic nature of hand gestures, and determination of the start and end points of each gesture becomes difficult in a video stream required in the recognition system. For diverse range of applications, researchers have undertaken vision-based gesture recognition studies. In this current research, deep learning approach is employed to develop a novel model specifically designed for the classification of sign language. Our study focuses on generating datasets comprising videos of Myanmar sign language (MSL) at the word level, incorporating various lighting conditions and complex backgrounds. The implementation of a Convolutional Neural Network (CNN) model allows us to present a unique approach to hand gesture recognition, using a comprehensive process involving data collection, parameter analysis, CNN model creation, as well as training and testing phases, aimed at facilitating precise recognition tasks. Notably, the utilization of the Stochastic Gradient Descent with Momentum (SGDM) optimizer resulted in a remarkable 98% success rate, while the Adam optimizer demonstrated an even higher success rate of 99%. Therefore, our proposed system is well-suited for the development of wearable real-time systems capable of automatically recognizing MSL. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Classification Convolutional neural network Deep learning Hand gestures Myanmar sign language (MSL) recognition

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Integration of IoT-enabled UAVs in Smart Agriculture: Types, Applications, Trends, challenges 7

Integration of IoT-enabled UAVs in Smart Agriculture: Types,...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Prathyusha, Gajawada Krishnan, M.B.Mukesh Deepak, S.Prem Kumar SRM Institute of Science and Technology School of Computing Department of Networking and Communications India B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for various applications, including the military, smart agriculture, rescue operations, entertainment, monitoring, and medicine delivery. In the current era, UAVs extensively employ a wide range of technologies to transmit data to base stations. The collected data undergoes processing, producing remarkable outcomes through the integration of machine learning and deep learning technologies. This paper reviews disease identification and object detection techniques and their associated applications & challenges. Additionally, the research explores recent trends and technologies in the field of smart agriculture. This research emphasizes the use of sensors in unmanned aerial vehicles (UAVs) by analyzing the methods utilized for recognizing diseased areas and things while identifying insects and sick crops. This paper also addresses the challenges encountered by existing methodologies and proposes directions for future research and development. Furthermore, this review delves into suitable techniques for heterogeneous UAVs and examines IoT-related strategies. © 2024 IEEE.

关键词： Smart agriculture

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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Decentering the Designer Through Live-Action Roleplay 24

Decentering the Designer Through Live-Action Roleplay

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2024 Halfway to the Future Symposium, HttF 2024

作者： O’Bryan, Mikaela Alvarez, Alberto Font, Jose Robinson, Raquel Breejon Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Sweden Game Lab Department of Computer Science and Media Technology Malmö University Sweden IT University of Copenhagen Denmark

ISBN: (纸本)9798400710421

More-than-human approaches to HCI design research have gained traction in recent years, facilitating a need to employ new methods that decenter the human perspective. We propose live-action roleplay (larp), as a method and practice within HCI that can support perspective taking and empathy-building with non-humans actors. In this paper, we discuss a local community workshop where we facilitated numerous body-based and larp activities surrounding the theme of sustainability, and reflect on the strengths larp offers in service of decentering the human perspective. © 2024 Copyright held by the owner/author(s).

关键词： Design for manufacturability

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Enhancing Fairness and Performance: Scalable Hybrid Solutions for Class Imbalance in Big Data Analytics 3

Enhancing Fairness and Performance: Scalable Hybrid Solution...

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

作者： Puneetha, B.H. Kumar, Manoj M.V. Prashanth, B.S. Kumara, Ajay M.A. Department of Computer Science and Business Systems Bapuji Institute of Engineering and Technology Karnataka Davanagere India Department of Information Science and Engg Nitte Meenakshi Institute of Technology Karnataka Bangalore560064 India Department of Computer Science University of North Carolina Wilmington United States

Recent advancements in handling class imbalance include hybrid approaches that combine data-level and algorithmic-level techniques. However, challenges such as computational efficiency and adaptability to dynamic datasets remain to be prominently addredssed. This research paper introduces novel hybrid techniques that combine both data-level and algorithmic-level approaches to address class imbalance in large datasets. These techniques are designed to improve class distribution and processing efficiency while enhancing prediction accuracy, particularly for minority classes. The proposed model applies K-means SMOTE for over-sampling the minority class and centroid-based undersampling for the majority class, followed by Classification using XGBoost. Evaluations on multiple datasets demonstrate significant improvements in precision, recall, AUC-ROC, and F1-score compared to traditional methods. In addition to enhancing robustness and fairness, the approach effectively addresses challenges related to scalability, data diversity, and dynamic data sources in big data analytics. The hybrid technique achieved a 10-20% improvement in key performance metrics, including precision, recall, F1-score, and AUC-ROC, across various UCI datasets such as Diabetes, Hepatitis, and German Credit, confirming the model's superiority in handling class imbalance and improving overall Classification performance. © 2025 The Author(s).

关键词： Large datasets

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FAMMNPB: Design of an Enhanced Forensic Analysis Model with Multidomain Feature Extraction and Advanced Neural Architecture for Permissioned Blockchains 9

FAMMNPB: Design of an Enhanced Forensic Analysis Model with ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Mate, Ashwini Y. Khandare, Kiran S. Hassan, Mohammad Bode, Kiran Nikesh Bode, Nikesh S. Missal, Karishma K. Nagpur India Nagpur India J D College of Engineering And Technology Department of Electronics And Telecommunication Nagpur India J D College of Engineering And Technology Department of Computer Science And Engineering Nagpur India Suryodaya College of Engineering And Technology Department of Computer Science And Engineering Nagpur India J D College of Engineering And Technology Department of Information Technology Nagpur India

ISBN: (纸本)9798350394474

This work introduces a novel approach that leverages multidomain feature extraction coupled with a 1D Convolutional Neural Network (CNN) for robust anomaly detection. To address the challenge of transaction delay predictions, we employ a Bi-directional Gated Recurrent Unit (BiGRU) RNN, for its capability to capture temporal sequences with high precision. For the task of chain splitting, the Teaching-Learning-based Optimization (TLbO) technique is used to ensure the efficient creation of sidechains. Further, we integrated DeepSHAP, in tandem with 1D CNN and BiGRU, to enhance the performance of our models, fostering better trust in the results. Our proposed methodology yields notable improvements over existing techniques. As we observed enhancements in the precision of anomaly detection by 4.9%, accuracy in detecting anomalous events by 3.5%, Recall by 4.5% and AUC by 3.9%. These advancements fortify the security posture of permissioned blockchains and optimize their operational efficacy, thus marking a significant improvement in blockchain forensics. © 2024 IEEE.

关键词： Feature extraction

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Bengali News Abstractive Summarization: T5 Transformer and Hybrid Approach

Bengali News Abstractive Summarization: T5 Transformer and H...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Hasib, Khan Md Rahman, Md. Atiqur Masum, Mustavi Ibne Boer, Friso De Azam, Sami Karim, Asif Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Charles Darwin University Faculty of Science and Technology NT Australia

ISBN: (纸本)9798350382204

In today's fast-paced world, everyone wants things to happen quickly. Thanks to the internet, news spreads super fast. But not all news is important. News summarization helps by giving a short version of each news story, so readers can easily figure out what type of news they want to read. There are two main types of summarization: Abstractive Text Summarization and Extractive Text Summarization. The process of abstractive text summarization is much more complex than that of extractive text summarization. This study proposes a model for generating extractive summaries, which are then utilized as input to generate abstractive summaries. The model uses the Bengali Text Summarization (BenSumm) model for extractive summarization and the Bangla Text-to-Text Transfer Transformer (BanlaT5) for abstractive summarization. The research also compares summarization acquired straight from the BanglaT5 model with summarization obtained via the proposed model. Abstractive summarization in the Bengali language has been accomplished using the Text-to-Text Transfer Transformer(T5) in this research. Although abstractive summarization of the Bengali language has been accomplished over the years using a variety of techniques, the field of using T5 in this field has only recently been discovered, and there is still a wide range of opportunities to be explored. The study has achieved promising results. © 2023 IEEE.

关键词： Text processing

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework 4

Preprocessing and feature extraction of MRI Liver Tumour ima...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Bhagya, A. Perumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350349856

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer Vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. The multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. The Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. The new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep Learning Enabled Secure IoT Module for Smart Agriculture in Diverse Environmental Conditions 14

Deep Learning Enabled Secure IoT Module for Smart Agricultur...

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

作者： Ansari, Shaharyar Alam Ali, Salman Luqman, Mohammad Ahmad, Shahnawaz School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science Aligrah Muslim University Aligarh India

ISBN: (纸本)9798331531935

The Internet of Things (IoT) stands as a revolutionary technology which impacts smart agriculture and different sectors beyond it. The research describes an intelligent IoT-enabled monitoring system which uses sensors to bring improved agricultural performance through environmental real-time observations combined with artificial intelligence decision systems. The proposed system completes its operation by executing sensor data acquisition followed by deep learning-based analysis and secure data transmission procedures. The initial stage of IoT-enabled operation utilizes environment-sensing devices which collect essential parameters for precision farming. A Recurrent Neural Network (RNN) processing system applies classification methods to collected data to generate useful insights. Secure cloud data transmission takes place through an encryption system which combines Substitution-Caesar cipher with Improved Elliptic Curve Cryptography (IECC). The system demonstrates 99.64% accuracy, and the proposed encryption method proves efficiency through its average execution times of encryption at 0.3904 ms and decryption at 0.1102 ms which exceeds current models in speed and security performance. © 2025 IEEE.

关键词： Recurrent neural networks

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