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IPH2O: Island Parallel-Harris Hawks Optimizer-Based CLSTM for Stock Price Movement Prediction

Annals of Data science 2024年第6期11卷 1959-1974页

作者： Joel, Linda Parthasarathy, S. Venkatesan, P. Nandhini, S. Department of Mathematics SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai89 India Research and Statistics Sri Ramachandra Medical College and RI Sri Ramachandra University Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Stock price movement forecasting is the process of predicting the future price of a financial and company stock from chaotic data. In recent years, many financial institutions and academics have shown interest in stock market forecasting. The accurate and successful predictions of the future price of stock yield a substantial profit. However, the current approaches are a major challenge due to the dynamic, chaotic, high-noise, non-linear, highly complex, and nonparametric characteristics of stock data. Furthermore, it is not sufficient to consider only the target firms' information because the stock prices of the target firms may be influenced by their related firms. Significant profits can be made by correct forecasting of stock prices, while poor forecasts can cause huge problems. Thus, we propose a novel Island Parallel-Harris Hawks Optimizer (IP-HHO)-optimized Convolutional Long Short Term Memory (ConvLSTM) with an autocorrelation model to predict stock price movement. Then, using the IP-HHO algorithm, the hyperparameters of ConvLSTM are optimized to minimize the Mean Absolute Percentage Error (MAPE). Four different types of financial time series datasets are utilized to validate the performance of the evaluation measures such as root mean square error, MAPE, Index of Agreement, accuracy, and F1 score. The results show that the IP-HHO-optimized ConvLSTM model outperforms others by improving the prediction rate accuracy and effectively minimizing the MAPE rate by 19.62%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Profitability

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A CNN Ensemble Approach to Provide Non-combat and Support Roles Training Deaf and Dumb Individuals using Hand Signs 14

A CNN Ensemble Approach to Provide Non-combat and Support Ro...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman, Arifur Mahmud, Anik Shill, Pintu Chandra Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

Appointing deaf and dumb individuals in military services is very challenging, as it is very difficult process to provide them training. Machine learning model is a useful tool for providing effective non-combat and support roles training to deaf and dumb individuals through hand signs interpreters. We have developed a CNN Ensemble approach to recognize and classify various hand signs used in during training sessions. This can help interpreters to quickly and accurately interpret the commands given by military trainers, making training more efficient and effective. We have made a comprehensive dataset for this research purpose. We have designed this dataset for 35 common commands used by military trainers during training. For each of command class we have taken 160 images. So our dataset consists of total 5600 images. To design our final model, we have proposed two Convolutional Neural Networks model. Then we have imported VGG19 using TensorFlow and Keras libraries. We have applied these three models on our dataset. To get better performance we have also applied Voting Ensemble model on these three models. We have also combined these three models to make an Average CNN model and applied several classifier models such as SVM, RF, KNN and DT on that model. As we have noticed, Voting Ensemble model performed better than all these models with highest accuracy 94.20%. We believe that our proposed models will bring a great impact in defence sector. © 2023 IEEE.

关键词： Convolutional neural networks

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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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Secure blockchain bidding auction protocol against malicious adversaries

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High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

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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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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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Multimodal Classification via Visual and Lingual Feature Layer Fusion 3rd

Multimodal Classification via Visual and Lingual Feature L...

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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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A Comprehensive Von Willebrand Disease Awareness and Support Chatbot for Senegalese Communities 39

A Comprehensive Von Willebrand Disease Awareness and Support...

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39th International Conference on Information Networking, ICOIN 2025

作者： Akuthota, Keerthi Sai Reddy, C. Kishor Kumar Shuaib, Mohammed Alam, Shadab Alshanketi, Faisal Kyatham, Aashritha Reddy Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331506940

A lack of the protein required for healthy blood coagulation results in Von Willebrand disease (VWD), a blood condition. The absence of readily available, thorough information to assist individuals with VWD in managing their illness is one of their biggest problems. Many patients believe they lack access to all the necessary resources, which is made worse by language and technology literacy challenges, especially in places like Senegal, where French and Wolof are the main languages. Senegal is a small coastal African country. Senegalese are made up of at least six ethnic groups, with the Wolof being the largest. With a particular focus on the Senegalese community, creating an educational intelligent chatbot for VWD sufferers seeks to address these issues by improving literacy and filling in knowledge gaps. Users can communicate with the system regardless of their technological expertise thanks to this chatbot's use of speech recognition and machine learning technologies to provide precise, culturally relevant, and understandable information in both French and Wolof. The chatbot is intended to significantly improve the quality of life for VWD patients, especially in Senegal, by providing dependable, current materials in a safe online setting, equipping them with the knowledge they need to manage their illnesses properly. Through an inventive and user-friendly platform, the chatbot seeks to address the pressing need for more education and awareness of VWD, enhance illness management, and offer reliable educational resources. With the use of cutting-edge technology, the chatbot makes sure that information is presented in an approachable and culturally appropriate way, which facilitates patients' comprehension and use of the advice in their everyday lives and, eventually, improves their capacity to manage VWD. © 2025 IEEE.

关键词： Automated Speech Recognition Literacy ChatBot Educational Chatbot Machine learning Senegalese Von Willebrand Disease

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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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A Review on the Impacts of Social Media on the Mental Health 6th

A Review on the Impacts of Social Media on the Mental Health

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Tapu, Md. Abu Bakar Siddiq Akash, Rashik Shahriar Al Fahim, Hafiz Jarin, Tanin Mohammad Bhuiyan, Touhid Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Birulia Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031501579

There are numerous effects of social media use on people’s daily lives. Every day we are connected a lot of time with social media. As a result, our brains become unbalanced, and we feel a lot of illness in our bodies. As a result, the primary objective of this analysis is to provide information on how social media affects its users. Nineteen studies were included in this paper regarding the main purpose. We categorized the papers into four types, Status and Post based research, Research paper and Database analysis-based research, Survey based research, and disease-based research. After a comprehensive analysis of the available literature, we have concluded that utilizing the social side has an effect on our mental health and actions. The results highlighted a number of critical aspects, such as the varied approaches to determining the influence of social media, the limitations of the studies, and our thoughts on what should be enhanced. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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