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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kaushik, Anu Garg, Deepanshu Nehra, Anushka Bali, Rasmeet S Baza, Mohamed Srivastava, Gautam Chandigarh University Computer Science & Engineering Department India Thapar Institute of Engineering and Technology Computer Science & Engineering Department India College of Charleston Department of Computer SC29414 United States Brandon University Department of Math and Computer Science Canada

ISBN: (纸本)9781665480451

Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput. © 2022 IEEE.

关键词： Autonomous vehicles

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Tweeting the Market: Leveraging LSTM for Stock Price Prediction 2

Tweeting the Market: Leveraging LSTM for Stock Price Predict...

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

作者： Modi, Vinni Agrawal, Nandini Sardana, Neetu Jain, Ishika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology U.P Noida India

ISBN: (纸本)9798331541217

Investors and traders constantly seek an edge, a source of insight that can help them predict market movements more accurately. Over the past several years, the emergence of social media and the increase in use of platforms such as Twitter, Facebook, etc. has given birth to a new dimension of market analysis - sentiment analysis and its potential effect on stock prices. The mood dimensions that correlate with market movements can be complex and multifaceted. Only a few researches have studied the effect of social media on stock price predictions. Prior investigations have examined discussions surrounding the COVID-19 virus, its societal repercussions, and its potential sway over financial markets. This research has specifically scrutinized the effect of negative sentiments and their potential repercussions on stock prices of major tech companies like Microsoft, Google, and Tesla. The paper aims to identify the most effective model among ARIMA and LSTM for predicting stock prices by correlating them with sentiments expressed in social media posts. The motive of this paper is to help investors better analyze the market and adjust their strategies accordingly to earn more profits. © 2024 IEEE.

关键词： Tweets

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Optimizing Healthcare Accessibility and Effectiveness through the Implementation of AI Solutions for Remote Patient Monitoring 3

Optimizing Healthcare Accessibility and Effectiveness throug...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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Using Web Mining to Track and Predict the Spread of Diseases 11

Using Web Mining to Track and Predict the Spread of Diseases

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11th International Conference on Bio Signals, Images, and Instrumentation, ICBSII 2025

作者： Khan, Sadik Institute of Engineering & Technology Bundelkhand University Department of Computer Science & Engineering Jhansi 284128 India

ISBN: (纸本)9798331513863

These days, we're practically swimming in online data - from the quick updates, our friends share on social media to the nonstop flood of news stories and everything else in between. Web mining has turned into a go-to method for digging through all this information and finding patterns that matter. One place where this really proves its worth is in public health, especially when it comes to keeping an eye on infectious diseases and predicting how they might spread in real time. This piece takes a close look at how web mining methods can be used to monitor disease outbreaks. First, it explores different tools and research on web-based surveillance, focusing on how they can quickly catch early warning signs of potential epidemics. Then, it walks through a step-by-step framework for collecting and analyzing data from various online sources in near real-time - think social media signals, location data, and time-based trends. To illustrate these ideas, the study draws on a public dataset from Kaggle and uses features like geolocation and social media activity to train machine learning models that can predict the spread of diseases. The results suggest that when you combine web-scraped information with traditional epidemiological data, early detection and prediction become a lot more accurate. This finding is really important for public health because it shows how big data analytics can guide everything from how resources are allocated to when policies should be put in place. Overall, web mining emerges as a valuable tool for delivering timely insights and interventions, potentially saving lives and optimizing public health strategies. © 2025 IEEE.

关键词： disease surveillance machine learning online data predictive analytics public health informatics web mining

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Lung Cancer Classification Using Machine Learning 1

Lung Cancer Classification Using Machine Learning

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Sachdeva, Ravi Kumar Bathla, Priyanka Rani, Pooja Lamba, Rohit Ghantasala, G. S. Pradeep Rajesh Sharma, R. Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science & Engineering Punjab India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Mmictbm Haryana Ambala India Mmec Haryana Ambala India Alliance University Alliance College of Engineering and Design Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350376517

Lung cancer is leading among nineteen different types of cancer deaths across the world. Later the lung cancer is detected, the lesser the chances of survival for an individual. The authors have proposed a model to predict lung cancer in the initial stages. To build the model, the authors applied seven different classifiers: Naive Bayes (NB), K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Adaboost, Logistic Regression (LR), and Xgboost, to the lung cancer dataset accessible on Kaggle. The authors further applied class balancing using the SMOTE technique and observed the effectiveness of classifiers. The authors found that the class balancing improved the performance. LR has achieved the highest accuracy (96.30%) among all applied classifiers after applying class balancing. © 2024 IEEE.

关键词： Lung cancer

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Investigating Machine Learning (ML) in Augmented and Virtual Reality: A Comprehensive Analysis in Prawn Farming

Investigating Machine Learning (ML) in Augmented and Virtual...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Painuly, Vaibhavi Kavitha, R. Ravela, Ratna Raju Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India Department of Electronical & Electronics Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

Commercial lakes ought to be kept up well in arrange to meet the expanding request for prawns universally and create at their best. This think about addresses the challenge by combining present day sensor innovation, expanded reality interfacing, and machine learning calculations. The plan employments state-of-the-art sensors to ceaselessly screen water quality, empowering proactive decision-making. Machine learning calculations analyze collected information to expect lake conditions, giving agriculturists with a see of potential future occasions. Increased reality representation encourages way better information collection and real-time decision-making through intuitively interfaces. This all-encompassing approach progresses efficiency, maintainability, and effectiveness within the prawn cultivating industry by revolutionizing current hones. © 2024 IEEE.

关键词： Adversarial machine learning

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R²CS: a hybrid cryptosystem using RC-5 and RSA for secure transmission of satellite hyperspectral data

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Iran Journal of computer science 2025年 1-17页

作者： Ramamoorthy, A. Dutta, Anurag Lakshmi, M. Gayathri Department of Mathematics St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Government College of Engineering and Textile Technology Calcutta Serampore India Department of Mathematics Saveetha Engineering College Tamil Nadu Chennai India

Secure transmission of hyperspectral data from satellites is crucial for astronomical and remote sensing applications. Traditional methods like Rivest-Shamir-Adleman (RSA) and Rivest Cipher (RC-5) support the process but have some shortcomings;for example, if the dataset to be secured is large enough, RSA is very slow;similarly, if RC-5 was used for the same, it might make the process vulnerable to attacks. This research introduces R²CS, a hybrid cryptosystem that combines RC-5 and RSA to improve both security and speed for encrypting satellite hyperspectral data. R²CS first encrypts the data using RC-5 and complements it with an additional layer of protection from RSA. In this work, R²CS was evaluated on Chandrayaan-1’s Moon Mineralogy Mapper (M³) hyperspectral data (multidimensional nature, to validate versatility of R²CS), which contains 85 spectral bands. The results suggested that R²CS was able to achieve a data transmission rate of 100%, avoiding the data loss as suffered by standalone RSA. The average decryption–encryption cycle ratio is 1.137, i.e., R²CS is faster than RSA. These results suggest R²CS is a reliable method for protecting satellite data, offering a good balance between security and performance. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Chandrayaan-1 Cryptography Hyperspectral images Rivest Cipher (RC-5) Rivest–Shamir–Adleman (RSA)

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Maximizing Returns with Algorithmic Trading in the Banking using Improved AI algorithm 2

Maximizing Returns with Algorithmic Trading in the Banking u...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Yella, Anusha Yuvaraj, N. Att United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641062 India

ISBN: (纸本)9798350388916

Algorithmic trading, also referred to as alga buying and selling, is a technique of buying and selling financial property using pc algorithms and mathematical models. It's far unexpectedly turning into the favored method of buying and selling within the banking enterprise due to its capability to make brief and accurate trades at excessive volumes. In recent years, there has been an increasing call for progressed performance and profitability in banking operations, which has brought about the improvement of more excellent advanced AI algorithms for algorithmic trading. Those algorithms use gadget studying techniques to research vast quantities of information and make buying and selling choices based on real-time marketplace situations. Stepped forward, AI algorithms for algorithmic trading have the ability to significantly increase returns for banks by identifying profitable trading opportunities and executing trades in a fraction of the time it might take a human trader. This results in better trading volumes decreased risk, and advanced efficiency. One key advantage of algorithmic trading is its capacity to quickly adapt to changing market situations, permitting banks to live ahead of the curve and capitalize on marketplace movements. © 2024 IEEE.

关键词： Marketplaces

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Genetic Algorithm and Naïve Bayes-Based (GANB) Diabetes Mellitus Prediction System 1st

Genetic Algorithm and Naïve Bayes-Based (GANB) Diabetes Mel...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Yadav, Dharmveer Kumar Azad, Chandrashekhar Bala, Kanchan Sharma, Pradeep Kumar Kumar, Sujeet Computer Science and Engineering Katihar Engineering College Katihar India Computer Application Department Computer Science and Engineering NIT Jamshedpur India Sitamarhi Institute of Technology Sitamarhi India Computer Science and Engineering Katihar Engineering College Katihar India

ISBN: (纸本)9789811919053

Today, machine learning plays a significant role in the classification of healthcare conditions. Machine learning is the process of finding, discovering, and modeling massive amounts of data in order to identify unknown relationships and patterns that are useful to the decision makers. Medical data mining has begun to emerge exemplary, with the potential to discover interesting insights from medical domain datasets. These patterns may be used in the making of clinical decisions. In this research work, we proposed genetic algorithm and Naïve Bayes-based (GANB) diabetes mellitus prediction system. In which, genetic algorithm (GA) is used for feature selection and Naïve Bayes is used for prediction. The proposed system is trained using Pima Indian diabetes dataset (PIDD). The dataset is preprocessed using synthetic minority oversampling technique (SMOTE) for solving the issue of class imbalances. The effectiveness of the system is evaluated using classification accuracy (CA) and error. The simulation results show that the proposed GANB system achieves better accuracy as compared to the results of related earlier studies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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