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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Warbhe, Mohan K. Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

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. © 2024 IEEE.

关键词： Challenges of NLP in Healthcare Future treads of NLP for healthcare Healthcare NLP

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Enhancing Algorithmic Trading Strategies with Sentiment Analysis: A Reinforcement Learning Approach 3

Enhancing Algorithmic Trading Strategies with Sentiment Anal...

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

作者： Bharathi Mohan, G. Prasanna Kumar, R. Vifert Jenuben Daniel, V. Praghaadeesh, R. Maniappan, Varun Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University Botswana Faculty of Engineering and Technology Botho Botswana

ISBN: (纸本)9798350384598

This paper discusses the application of deep reinforcement learning algorithms in stock trading, where the stock market environment is highly dynamic in nature. To represent the dynamics of the stock market, news headlines are a key factor in order to predict whether the market direction is bullish (upward) or bearish (downward). So we use different sentiment analysis models like Finbert and Roberta to see how the sentiment scores given by these models affect our trading agent's strategy. These sentiment scores are added to our custom trading environment, where we combine these scores as part of the state along with other parameters like open, high, low, close prices and trading *** for the reinforcement learning part, we used various policy gradient methods like Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Deep Q-Network (DQN) to train our agent in this custom environment. These models were then back-tested on historic data, where the trading strategies devised by these deep RL agents during the training phase were applied. The results provided by these models were very promising. The evaluation metrics used were the Sharpe score and Sortino score. The highest Sharpe ratio was given by the PPO agent, which was 3.0 for Facebook data and 2.834 for Goldman Sachs data, using the Roberta sentiment model. Whereas for the Finbert model, PPO got 3.24 for Facebook data and 3.03 for Goldman Sachs data. This explains that the trading portfolio is performing really well. © 2024 IEEE.

关键词： Deep reinforcement learning

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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Bipartite Graph-Based Recommendation systems: Introducing the Enhanced Graph Convolutional Neural Network Method for Indonesian Tourism 7

Bipartite Graph-Based Recommendation Systems: Introducing th...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kurniadi, Felix Indra Sutomo, Rudi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Universitas Multimedia Nusantara Information Systems Faculty of Engineering & Informatics Banten15810 Indonesia

ISBN: (纸本)9798350307504

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth.. © 2023 IEEE.

关键词： Recommender systems

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Artificial Intelligent Applications in Smart Cities 4

Artificial Intelligent Applications in Smart Cities

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4th IEEE International Conference on Smart Information systems and Technologies, SIST 2024

作者： Caglayan, Ruhi Zafer Kayisli, Korhan Coteli, Resul Omirgaliyev, Ruslan Zhakiyev, Nurkhat Faculty of Engineering TED University Ankara Turkey Faculty of Engineering Gazi University Ankara Turkey Faculty of Technology Firat University Elazig Turkey Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350374865

History shows us that people move towards a more stable lifestyle after undergoing significant changes. During these times, the way communities and buildings were designed started to change too. Different periods in history are known for their standout moments, each bringing its own set of needs and tech breakthroughs. Nowadays, tech keeps evolving. People nowadays aim for a more straightforward life, and technology has been shaping up to meet this need. We live in times highlighted by the rise of smart technology, like artificial intelligence and the Internet of Things, which not only make daily tasks easier but also transform how we think about our cities and personal spaces. This piece focuses on exploring the smart city idea, touching upon various associated subjects. As we delve into these areas, we'll also look at some important research related to them. © 2024 IEEE.

关键词： Artificial intelligence intelligence systems Internet of Things Smart City

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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An Empirical Study of Machine Learning Techniques for ODI Bowler Performance Evaluation 4

An Empirical Study of Machine Learning Techniques for ODI Bo...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Das, Biswaranjan Das, Nayan Ranjan Mondal, Bappaditya Sur, Ratul Paul, Sushmita Das, Basudha Maity, Sutanu Academy of Technology Department of Engineering Science & Humanities West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Engineering West Bengal Hooghly712121 India Academy of Technology Department of Computer Science & Business Systems West Bengal Hooghly712121 India

ISBN: (纸本)9798350389128

The objective of the paper is to construct an innovative ranking system based on various Machine Learning (ML) techniques specifically tailored for One Day International (ODI) male players. It aims to create a recommender system that produces bowler rankings closely aligned with the International Cricket Council (ICC) Rankings, with a focus on enhancing accuracy and reliability. The dataset encompasses a wide range of match parameters, performance metrics, and player statistics, providing a comprehensive foundation for insightful analysis. By employing data-driven techniques, the paper aims to highlight key performance indicators that significantly influence bowler rankings. We performed an analysis among five machine learning models that can produce rankings of bowlers and select the best model whose predictions are closest to the ICC's official rankings. The rankings obtained from the five machine learning algorithms have been compared with ICC rankings utilizing the Spearman Rank Correlation Coefficient. The results highlight that the Artificial Neural Networks (ANN) exhibit the best outcomes with a rank correlation of 0.7550. © 2024 IEEE.

关键词： Adversarial machine learning

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Arduino-Based Real-Time Obstacle Detection in Vehicles Using Proximity Sensors

Arduino-Based Real-Time Obstacle Detection in Vehicles Using...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Gupta, Meenu Kumar, Rakesh Das, Sunaina Sharma, Rohan Obaid, Ahmed J. Chandigarh University Department of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq Al-Ayen University Technical Engineering College Department of Computer Technical Engineering Thi-Qar Iraq

ISBN: (纸本)9798350344387

Vehicle collision is currently one of the major problems for fatal traffic accidents where people lose their lives. To avoid this issue, intelligent vehicles pay more attention to people. The current anti-collision vehicles are lacking with the black path follower, and it is a high risk of loss of human lives. To overcome these challenges, an Arduino-based anti-collision device is suggested to reduce the count of accidents to avoid head-to-back vehicle collisions. Nowadays, one of the evolving technologies in the automotive sector is detecting obstacles using anti-collision vehicle systems. In the automobile sector, an anti-collision system is a safety system that automatically prevents collision among cars and objects. In this work, a microcontroller-based automated car anti-collision system is proposed. The proposed system is skilled at spotting obstacles using a precise distance sensor, alerting the driver when a collision is imminent, and automatically applying the brakes via an actuator, when necessary, without the driver's assistance. The proposed system will automatically stop the vehicle in case the driver avoids the collision as the system monitors the vehicle's condition continuously. This system can safeguard drivers from mishaps and lower accidental death rates because it is user-friendly and adaptable. This mechanism can be used in any automobile vehicle as it's a prudent system. © 2023 IEEE.

关键词： Accident Anti-collision Arduino Obstacle Detection Proximity Sensors Risk Ultrasonic Sensor Cars Vehicle

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Dermoscopic Skin Lesion Classification using Color Features Based on Fine-Tuning of Resnet50 and Densenet121 Transfer Learning Models

Dermoscopic Skin Lesion Classification using Color Features ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Kujani, T. Kumar, V. Dhilip Bhuvanya, R. Kavitha, A. Sathya, T. Sathiya Priya, S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Chennai India Government Polytechnic College Computer Engineering Karur India Sri Sairam Engineering College Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350360660

Skin cancer, which stands as the most prevalent form of human malignancy, is primarily identified through visual examination. The procedure for identifying skin lesions typically starts with an initial clinical assessment, which might be followed by dermoscopic analysis, a biopsy, and a subsequent histopathological checkup. Image-based Skin lesion classification presents a significant challenge due to the subtle and intricate variations in their appearance. Deep Convolutional Neural Network (DCNN) show promising results in managing diverse and intricate tasks involving a wide array of finely detailed object categories. Skin lesions Classification is shown in this work initially using a six-layered CNN performed on the skin cancer dataset to classify seven classes of lesions with 10015 images. Exploratory data analysis of the dataset is performed to identify any outliers and to verify the balance of samples in each class. It was observed after 50 epochs, the training accuracy was 98.03% and the testing accuracy was 67.40%. To avoid the overfitting observed in the CNN approach, the pre-trained transfer learning models, Resnet50 and Densenet121 were finetuned with data augmentation to handle the imbalance of samples present in seven classes. The results obtained prove that the Densenet121 model achieves high performance compared with the other Resnet50 model after augmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Microgrid Energy Security Through Optimal Power Sharing in Multi-Modal Energy Storage systems 4

Enhancing Microgrid Energy Security Through Optimal Power Sh...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Anik, Shaikh Tawhidul Islam Mithil, Md Salman Khan Ahmad, Shameem Hazari, Md. Rifat Islam, Mahmoodul Ahmed, Tofael Parvez, Md. Shahariar Hassan, Mahamudul American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Chittagong University of Engineering & Technology Department of Electrical and Electronic Engineering Chattogram4349 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Industrial and Production Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Microgrids (MGs), which combine distributed energy resources (DERs) with energy storage systems (ESS), offer improved energy security, reliability and sustainability by utilizing renewable energy sources for subsequent use. However, single ESS options often face challenges such as short life cycles, low power density etc. More than one ESS can resolve the mentioned issues though it increases complexity and maintaining stability becomes an issue. This paper proposes an effective energy management strategy between two ESS namely battery and supercapacitor to increase energy security in MG. The proposed strategy is designed in such a way that one of the two ESS stays connected all the time to the DC bus to ensure uninterruptable power supply to loads. Due to the use of DC bus, the system complexity decreases as there is no need to control frequency. Two cases were considered to verify the performance of the proposed energy management strategy in Matlab/Simulink. In the first case, during the unavailability of supercapacitor, the battery supplies 50 W to the load in addition to PV whereas supercapacitor supplies 136 W in the absence of battery along with PV. Furthermore, it was shown that the absence of PV has no negative effect in terms of power flow by ESS to meet load demand. © 2025 IEEE.

关键词： Energy security Energy Storage systems Microgrid Power sharing Supercapacitor

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