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3rd International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Saklani, Avantika Pannu, H.S. Tiwari, Shailendra Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (纸本)9789819926015

Multimedia content has been widely available over social media and web services. Recently, the growth of multimedia data has presented new obstacles and great prospects for the multimedia community. Mostly, the content through the web comes in a multimodal manner, and images are generally accompanied by text. This paper presents a new multimodal classification framework involving images and their related texts. More precisely, it presents the fusion of textual and visual features with a batch normalisation technique to handle multimodal data in an efficient way. The proposed work is validated using the large-scale food dataset as the standard benchmark for multimodal food classification. The results show that the proposed model outperforms the text-only, image-only models and the state-of-the-art results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Classification (of information)

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Research on Secure Blockchain Digital Resume Storage Systems

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International Journal of Network Security 2025年第3期27卷 631-638页

作者： Hwang, Min-Shiang Chang, Yung-Ling Wu, Nan-I Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Artificial intelligence Asia University Taichung41354 Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan

The study primarily aims to achieve three goals: first, to research and develop a consensus mechanism for voting among blockchain nodes. The research results will enhance the existing equity-based voting mechanism, improve the voting selection mechanism of the extended type of equity certification mechanism (Delegated Proof-of-Stake, DPOS), and utilize the entrusted proof of equity to address its deficiencies. The major shareholder of the equity certification mechanism, the bank, makes money. Fewer nodes can vote for representatives to participate in the blockchain packaging competition, which increases the chances of winning and then distributes profits. The election method of agents and how to achieve fairness in subsequent distribution are the focus of this year’s research. Second, to study the innovative distributed storage system of dual blockchain and utilize blockchain technology combined with credit to enhance the credibility and security of production and medical records. The research’s main themes are the production history and medical care before the establishment of the blockchain. Personal information, such as health records, must be processed to maintain the efficiency of both personal information and system processing. How consumers or medical units will utilize this information to obtain accurate production and sales history records and medical records, and whether this information can be credible through original unit authentication without a third-party verification. These are the key points of this project. Third, to combine the development of digital currency with an innovative resume preservation system. The blockchain system will be a digital currency issued on a private chain. A smart contract will be written according to the blockchain public specification ERC20 to establish a set of resumes. The data is saved and packaged into the complete digital currency innovation history preservation system of the operational blockchain node network. By

关键词： Blockchain

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Obesity Risk Prediction Using Machine Learning Approach 1

Obesity Risk Prediction Using Machine Learning Approach

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Maria, A.S. Sunder, R. Kumar, R.Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350336009

Approximately about two billion peoples are affected by obesity that has drawn significant attention on social media. As the sedentary lifestyle which includes consumption of junk foods, no physical activities,spending more on screen,etc are one of the causes of *** generally refers to that a person's body possessing an excessive amount of *** is a huge increase in obesity cases which resulting cardiac problems,stroke,insomnia, breathing problems,***-2 diabetes has been detected in the patients suffering from obesity recently. The studies showing that there are lot of young individuals and children's who has been suffering from overweight and obesity issues in Bangladesh. Here, a strategy for predicting the risk of obesity is proposed that makes use of various machine learning methods. The dataset Obesity and Lifestyle taken from Kaggle site which is collection of different data based on the eating habits and physical conditions,such as height, weight,calorie intake,physical activities are just a few of the 17 different categories in the dataset that reflect the elements that cause obesity. Several machine learning methods include Gradient Boosting Classifier, Adaptive Boosting (ADA boosting), K-nearest Neighbor (K-NN), Support Vector Machine (SVM), Random Forest, and Decision Tree. A few important performance factors are used to group the models. Predicting the levels of high, medium, and low obesity in this case using the experimental results. The gradient boosting techniques have the highest accuracy 97.08% in comparison to other classifiers © 2023 IEEE.

关键词： Support vector machines

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Hybrid Vision Transformer and CNN for Detection of Overripe Coffee Berry Disease (OCBD) in Coffee Plantation 2

Hybrid Vision Transformer and CNN for Detection of Overripe ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvanarayanan, Raveena Surendran, R. Gomathi, T. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

The article proposes an innovative Hybrid Vision Transformer and CNN architecture for the precise identification of Overripe Coffee Berry Disease (OCBD) in coffee farms. The suggested model adeptly integrates the advantages of Vision Transformers and CNNs, allowing it to capture both the global and local characteristics of coffee berries. Experimental findings indicate that the Hybrid model outperforms conventional CNN-based methods, with an impressive accuracy of 96.7% on a varied dataset. The model's resilience to fluctuating illumination conditions and obstructions makes it a viable alternative for practical applications in coffee plantation management. © 2024 IEEE.

关键词： Image segmentation

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Enhanced Predictive Model to Diagnose Coronary Heart Disease Using Decision Tree 2

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Joshi, Sujata Dixit, Ananya Nair, Mydhili K. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. Institute of Technology and Management Department of Information Science and Engineering Bangalore India School of Computer Science Rv University Bangalore India

ISBN: (纸本)9798350373110

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global Burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from M. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced-Pred-Model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance. © 2024 IEEE.

关键词： Prediction models

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Towards efficient generative AI and beyond-AI computing:New trends on ISSCC 2024 machine learning accelerators

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Journal of Semiconductors 2024年第4期45卷 12-15页

作者： Bohan Yang Jia Chen Fengbin Tu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and TechnologyHong KongChina School of the Gifted Young University of Science and Technology of ChinaHefei 230026Chin

Compared to the last decade when the convolution neu-ral network(CNN)dominated the research field,machine learn-ing(ML)algorithms have reached a pivotal moment called the generative artificial intelligence(AI)*** the emer-gence of large-scale foundation models[1],such as large multi-modal model(LMM)GPT-4[2]and text-to-image generative model DALL·E[3].

关键词： ISSCC beyond AI

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Enhanced Fake News Detection through the Fusion of Deep Learning and Repeat Vector Representations 2

Enhanced Fake News Detection through the Fusion of Deep Lear...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Mahmud, Tanjim Hasan, Imran Aziz, Mohammad Tarek Rahman, Taohidur Hossain, Mohammad Shahadat Andersson, Karl Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Pervasive and Mobile Computing Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350327533

The rapid dissemination of information across social networks, online news portals, and digital platforms has given rise to a pervasive issue - fake news. This misinformation, deliberately propagated by groups with ulterior motives, has the potential to divert societies in alarming directions. In a digital landscape inundated with millions of daily stories, distinguishing fact from fabrication has become a formidable challenge, casting a dark cloud of uncertainty over the information age. In response to this critical issue, this paper presents an innovative approach for enhanced fake news detection through the fusion of deep learning and Repeat Vector representations. In an era where individuals of all ages increasingly rely on online news sources, the dissemination of misinformation has become an alarming norm. It is a challenge to discern deceptive narratives that aim to sow discord and create undue *** confront this challenge head-on, we propose a deep neural network model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures using the Repeat Vector method. This hybrid model excels in deciphering complex patterns within 1D data, effectively tackling the menace of fake news. Our rigorous evaluation demonstrates that our proposed model outperforms contemporary methods, achieving an impressive validation accuracy of up to 98.94% on our *** a world increasingly plagued by misinformation, our approach represents a significant stride toward safeguarding the integrity of information in the digital age. © 2024 IEEE.

关键词： Autoencoder Technique Convolutional Neural Network Deep Learning Long Short-Term Memory

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An Efficient Method to Deal with Missing Values and Class Imbalance in Healthcare Data 5th

An Efficient Method to Deal with Missing Values and Class Im...

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

作者： Singh, Harpreet Singh, Birmohan Kaur, Manpreet Rani, Suvita Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India

ISBN: (纸本)9789819773701

The present era belongs to the age of digital devices, where everything is going to be digitized. This makes the massive production of data at faster rates and brings Big Data to light. The arrival of big data has influenced and reformed many domains like Healthcare, Bioinformatics, Agriculture, etc. Healthcare big data has gained the attention of various researchers as it aids in the early diagnosis and prediction of diseases. Generally, real-world healthcare data contains missingness and imbalance distribution of classes which makes classification a challenging problem. Therefore, an efficient method is introduced to deal with the problems of data incompleteness and class imbalance in healthcare data. A class mean and standard deviation-based imputation strategy is proposed to impute the missing values within the region of a particular class. Further, an Improved Elephant Herding Optimization (IEHO) algorithm is utilized to apply the optimal weights for improving the imbalanced learning in healthcare data. A new fitness function is proposed which considers the data missingness and class imbalance along with the feature weighting. The optimized k-Nearest Neighbor (k-NN) learning algorithm is used to perform the classification. The performance of the proposed method is evaluated on four healthcare datasets: Pima diabetes, Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival. A comparative analysis with state-of-the-art methods is performed based on four-performance metrics: accuracy, balanced accuracy, g-mean, and f-score. The proposed method obtained higher classification accuracy for Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival datasets than state-of-the-art methods. Experimental results validate the effectiveness of the proposed method in dealing with the missing values and class imbalance problems in healthcare data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Utilizing Transfer Learning and Class-Selective Image Processing for COVID-19 Detection 26

Utilizing Transfer Learning and Class-Selective Image Proces...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Tabassum, Noshin Parvin, Farjana Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

The year 2019 marked the emergence of a novel contagious illness named COVID-19 which was brought about by the SARS-CoV-2 virus. This newly identified virus swiftly spread across the globe, ultimately resulting in the unprecedented COVID-19 pandemic, that impacted populations, healthcare systems, and economies worldwide. Globally, there is a growing need for COVID-19 non-invasive detection by medical image analysis. In the realm of medical diagnostics, deep learning stands out as a supremely efficient approach for analyzing extensive collections of chest X-ray (CXR) images, particularly influencing the realm of COVID-19 screening. A specific technique within deep learning, known as Convolutional Neural Network (CNN), holds a preeminent position due to its effectiveness. In the scope of this research, Convolutional Neural Networks (CNN) have been utilized to differentiate COVID-19 cases from both normal cases and instances of Pneumonia, leveraging information extracted from chest X-ray images. This utilization underscores the potency of advanced machine learning in confronting pivotal healthcare hurdles. The approach involved fine-tuning a pre-trained CNN model, InceptionV3, via transfer learning, wherein certain layers were adjusted before training on the dataset. The initial classification accuracy exhibited strong performance across all image classes, except for one category that displayed a marginally lower accuracy rate. A straightforward yet efficient approach to enhance image contrast has been utilized exclusively in the lower-performance class. This was done to enhance the image quality within that particular class, leading to improved accuracy. Since the entire dataset did not have to go through image processing, the model was computationally efficient and yielded an overall accuracy of 99.92% outperforming existing works. © 2023 IEEE.

关键词： chest X-ray Convolutional Neural Network COVID-19 image processing Pneumonia transfer learning

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Survey on Asclepius: A User-Centric Chatbot Bridging the Gap in Healthcare Integrated with VPN technology 4

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shukla, Garima Sharma, Dolly Awasthi, Vanshaj Shekhawat, Deepika Shukla, Pavan Kumar Saranya, A. Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Graphic Era Hill University School of Computing Haldwani India Amity University Mumbai Department of Computer Science and Engineering Mumbai India Noida Institure of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350364729

This groundbreaking paper introduces Asclepius, a revolutionary software solution aimed at transforming early healthcare assistance through the seamless integration of cutting-edge technologies. At its core lies a sophisticated chatbot system, augmented with machine learning capabilities, facilitating swift and efficient connections between users and local medical professionals. With a strong emphasis on user anonymity and security, particularly concerning sensitive mental health concerns, Asclepius ensures utmost privacy throughout the interaction process. Constructed with meticulous attention to healthcare technology and security, Asclepius has been developed following extensive research across various domains, incorporating insights from acclaimed research articles to optimize and build an effective model. Central to its functionality is the utilization of state-of-the-art facial recognition technology and secure authentication protocols, instilling confidence in the system's reliability by enabling accurate user verification upon login. As of the publishing date, Asclepius has an 89% accuracy for its diagnoses by making use of a Supervised Decision tree-based Machine Learning Model. Through the incorporation of integrated virtual private network (VPN) technology, the project further establishes a secure communication channel between users and healthcare professionals, guaranteeing the protection of sensitive information and fostering a safe, confidential environment for seeking assistance. By seamlessly integrating a vast array of advanced technologies, Asclepius provides users with a comprehensive and user-friendly interface, prioritizing convenience and security in healthcare interactions and heralding a transformative shift in the landscape of early healthcare assistance. © 2024 IEEE.

关键词： Diagnosis

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