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artificial intelligence and Data engineering (AIDE), International Conference on

作者： Priyanshu Tiwari Soumya Patil S Ambareesh Rishabh Raj Dept. of Artificial Intelligence and Machine Learning Sir M. Visvesvaraya Institute of Technology Bengaluru India Dept. of Information Science and Engineering Sir M. Visvesvaraya Institute of Technology Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The unprecedented growth and market capitalization of the FAANG stocks - Facebook, Amazon, Apple, Netflix, and Google (Alphabet) - have mushroomed into the pay-off race of the leaders of the global financial market. Stock price volatility prediction attracts the focus of the investors and analysts in the pursuit of profit maximization relying on market movements. In order to enhance the precision of stock price predictions for FAANG companies, other more intricate methods for feature engineering are also coupled together with LSTM neural networks. Stock price dynamic intricacies are being reflected by the model using the complete set of financial indicators, predictive analytics, and market sentiment data as features. The work showed a phenomenal application of LSTM networks in the analysis and prediction of stock price trends compared to the classic methods on the sequential-by-nature front dealing with long dependencies. It is actually possible to build a strong base of data enabling the detection of such elusive trends in the performances of FAANG stocks and technical indicators, sentiment analysis, or economic metrics, for instance, thereby getting into working order for LSTM modeling. Results from experiments show the method provides better accuracy and reliability than conventional machine learning models, thus collecting valuable insights for certain financial-forecasting purposes. The paper serves as a demonstration illustrates how feature engineering and LSTM might be combined to improve predicting financial markets and provides one solution for investors, financial analysts, and policymakers who look to make informed investment decisions.

关键词： Analytical models Accuracy Data preprocessing machine learning Predictive models Market research Data models Forecasting Long short term memory Investment

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GAMe-BiLSTM: a novel modified metaheuristic deep learning technique for non-ST-segment elevation myocardial infarction classification

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International Journal of Information technology (Singapore) 2025年第2期17卷 1063-1078页

作者： Patil, Sachin Sambhaji Mohite-Patil, Tanajirao Bapuso Kalipindi, Murali Sarvani, Chalamalasetty Ramesh Babu, P. Maheshwari, Shikha Department of Electronics and Communication Engineering School of Engineering and Sciences MIT-ADT University Loni Kalbhor Maharashtra Pune India Department of Electronics and Telecommunication Engineering D. Y. Patil College of Engineering and Technology Maharashtra Kasaba Bawada Kolhapur India Department of Artificial Intelligence and Machine Learning Vijaya Institute of Technology for Women (Affiliated to JNTUK) Andhra Pradesh Enikepadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science College of Engineering and Technology Wollega University Oromia Region Nekemte Ethiopia Centre for Distance & amp Online Education Manipal University Rajasthan Jaipur India

Outdated approaches encounter challenges in discerning subtle electrocardiographic changes in real-time associated with Non-ST-Segment Elevation Myocardial Infarction (NSTEMI), leading to suboptimal outcomes, characterized by low accuracy, prolonged processing times, and slow convergence speeds. To address these issues, the introduced framework integrates a Graph Attention Meerkat based-Bidirectional Long Short Term Memory (GAMe-BiLSTM) to overcome interpretability limitations in traditional approaches, providing insights into critical clinical variables for more informed NSTEMI classification. GAMe-BiLSTM adapts to missing values, further enhancing model robustness. The effectiveness of the newly introduced NSTEMI classification approach is substantiated through an ablation experiment, where variations in loss functions, learning rates, and optimizers are systematically explored. This experimentation confirmed the superiority of the proposed classification method. This research underscores the integral role played by real-time electrocardiographic signal image data in enhancing the accuracy (99.99%) and efficacy of the introduced methodology, thereby contributing significantly to the advancement of NSTEMI classification techniques in the realm of cardiovascular research and diagnosis. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cardiovascular diagnostics Deep learning Electro cardio gram GAMe-BiLSTM Myocardial infarction

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Titanic Survivors Prediction Analysis Using machine learning Algorithms

Titanic Survivors Prediction Analysis Using Machine Learning...

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Computational intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： Shridhar B. Devamane Abhijith B H Bhushan Purushotham Ravindranath R. C Lakshmikantha G. C Udayaprasad P K Department of Computer Science and Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence and Machine Learning Global Academy of Technology Bangalore India Department of AI & ML Global Academy of Technology Bangalore India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive.

关键词： Support vector machines machine learning algorithms Accuracy Disasters Nearest neighbor methods Prediction algorithms Data models Vectors Sustainable development Random forests

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Real-time Human Action Recognition using Deep Convolutional Neural Networks 15

Real-time Human Action Recognition using Deep Convolutional ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vignesh, R. Balaraju, J. Poongothai, P. Rajput, Vaishali Rajkumar, N. Pratheep, V.G. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Karunya Nagar Coimbatore India Anuraag University Department of Cse Hyderabad India Department of Computer Science Bangalore India Vishwakarma Institute o Technology Computer engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Cse Thiruvalluvar Tamil Nadu Avadi India Kongu Engineering College Department of Mechatronics Engineering Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798350370249

current advances in deep gaining knowledge of have enabled automated systems to understand human moves from movies in actual-time. Deep convolutional neural networks (DCNNs) had been used to allow accurate and efficient real-time movement reputation. with the aid of leveraging the statistics-driven illustration mastering capability of DCNNs, movement popularity has become a powerful device for video analysis applications including robotics, surveillance, entertainment, augmented truth, and healthcare. DCNNs use an aggregate of densely connected convolutional layers and in addition completely linked layers for function extraction from films. The proposed architectures are usually skilled using supervised learning. Convolutional layers are used to understand nearby abstractions in motion pictures, which in turn are used to research worldwide features. diverse techniques together with temporal segmentation, movement-based characteristic extraction, and feature switch gaining knowledge of are also hired by means of DCNNs for achieving higher performance. recent works have investigated exceptional techniques to enhance the accuracy and speed of action reputation the use of DCNNs. This consists of techniques which includes model compression, decoupled inference networks, and efficient convolutional networks. By using combining those strategies, it's far viable to acquire higher popularity accuracy at the same time as at the equal time preserving an excessive computational speed. © 2024 IEEE.

关键词： Computational Equal Excessive Gaining Research Speed

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Smart Agriculture in the Era of Big Data: IoT-Assisted Pest Forecasting and Resource Optimization for Sustainable Farming

Smart Agriculture in the Era of Big Data: IoT-Assisted Pest ...

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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula Shwetha K S Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HW ACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption.

关键词： Smart agriculture Adaptation models Accuracy Biological system modeling Crops Pesticides Predictive models Prediction algorithms Cotton Random forests

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Empirical Evaluation of Deep learning based Models for Time Series Datasets 3

Empirical Evaluation of Deep Learning based Models for Time ...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Champaneria, Malav Panchal, Dhyani Bhalavat, Jiten Yadav, Hemant Bhatt, Nirav Prajapati, Purvi Bhatt, Nikita Smt. K. D. Patel Department of Information Technology CSPIT Charotar University of Science and Technology Gujarat Changa India Department of Artificial Intelligence and Machine Learning CSPIT Charotar Universityof Science and Technology Gujarat Changa India U & P U. Patel Department of Computer Engineering CSPIT Charotar University ofScience and Technology Gujarat Changa India

A deep learning-based method for predicting numerous time series is suggested inthis study. For example, this paper contrasts the effectiveness of DeepAR, N- Beats, Transformer, Temporal Fusion Transformer, and other deep learning models. Our test findings demonstrate that deep learning models can anticipate with excellent accuracy across a variety of time series datasets. Moreover, our results indicate that the ideal input representation differs among datasets and that it plays a significant impact in predicting accuracy. Overall, our work gives practical advice for choosing the appropriate model and input representation, as well as insights into the use of deep learning models for predicting numerous timeseries. © 2023 Elsevier B.V.. All rights reserved.

关键词： Time series

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Big Data Analysis on Intelligent Traffic Surveillance by Incorporating Sensors and IoT Devices on Roads 7

Big Data Analysis on Intelligent Traffic Surveillance by Inc...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Devi, A. Kapruwan, Akanksha Shrimali, Manish Appalakonda, Vengalapudi Maranan, Ramya Barve, Amit Tamil Nadu Coimbatore641 049 India Graphic Era Deemed To Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Computer Science And It Rajasthan Udaipur313 003 India Department of Artificial Intelligence And Machine Learning Kakinada Andhra Pradesh Surampalem533 437 India SIMATS Saveetha School of Engineering Department of Research And Innovation Tamil Nadu Chennai602105 India Parul University Parul Institute of Technology Faculty of Engineering And Technology Department of Computer Science And Engineering P.O.Limda Gujarat 391760 India

ISBN: (纸本)9798350359299

Rather than relying on public transportation, people are increasingly comfortable driving their automobiles to satisfy their mobility needs. Reasons for this include the automobiles' accessibility and the fact that they can be used whenever needed. Because of this, many cities around the world experience terrible traffic congestion and long wait periods at traffic signals. Smarter and more effective traffic management is possible through the integration of big data, the Internet of Things (IoT), and a cloud platform with a network of linked vehicles and sensors. The objective of this study is to estimate traffic conditions along a route and provide updates to the user. Initially, Deep learning (DL) models such as YOLO (You Only Look Once) and SSD (Single-Shot Object Detection) are developed and tested to detect vehicles. YOLO produces outstanding performance in vehicle detection, with an 89.5% mean Average Precision (mAP) and an 83.98% Intersection over Union (IoU). This model is then pushed to the cloud. A Global Positioning System (GPS) module is used to determine the user's current location. Based on the user's location, the nearest camera is enabled, and the recorded video is sent to the cloud. The cloud-deployed YOLO model is used to detect and count vehicles. To improve the user experience, this study has created a mobile application. The software contains a navigation map function that allows users to specify the maximum number of vehicles expected along their route. The created system counts the number of vehicles on their path, and if the count exceeds the specified threshold, the mobile app notifies the user about traffic conditions. © 2024 IEEE.

关键词： Network security

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing Using Modified AES

A Patient-Centric Blockchain Framework for Transparent and S...

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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Due to its decentralised, accessible, and secured structure, blockchain—a framework that has historically been viewed with suspicion—has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. machine learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains.

关键词： Privacy Ecosystems Smart contracts Medical services machine learning Blockchains Encryption Security Reliability Standards

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A Review on the Applications of Natural Language Processing in Healthcare

A Review on the Applications of Natural Language Processing ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： Mohan K. Warbhe Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards.

关键词： Sentiment analysis Ethics Data privacy Virtual assistants Semantics Natural language generation Medical services Natural language processing Medical diagnostic imaging Interoperability

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Anti-Accident Mechanism to Detect Driver Drowsiness Integrated with Alerting Mechanism: An IoT-based Model

Anti-Accident Mechanism to Detect Driver Drowsiness Integrat...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： Srujan Katukam Sai Vinay Potluri Sunell Dutt Abburi Rahul Cherukuri C Sreeka Parinishta Mohith Peddineni R. P. Ram Kumar Sanjeeva Polepaka Department of AI & ML Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India

According to the report released by the National Safety Council (NSC), about 1,00,000 accidents, 71,000 injuries, and 1,550 deaths are caused due to Drowsiness while driving each year. According to a study by the AAA Foundation for Road Safety, Drowsiness is responsible for up to 9.5% of accidents and 10.8% of those involving airbag deployment, injury, or significant property damage. The paper aims to build a real-time system to prevent accidents caused due to Drowsiness. It implements the features of Computer Vision, Deep learning, and Internet of Things (IoT) tools to detect Drowsiness and alert the Driver. The various alerting techniques adopted to alert the Driver includes Alarming, Vibration, and Water Sprinkler are implemented using IoT.

关键词： Vibrations Road safety Real-time systems Safety Internet of Things Brakes Injuries

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