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International Conference on information technology systems and Innovation (ICITSI)

作者： Jos Timanta Tarigan Avin Chaili Salim Anandhini Medianty Nababan Brian Wijaya Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

In role-playing games (RPGs), randomization plays an important role in designing the loot system. The unpredictability of rewards enhances player engagement and replayability. Thus, it is crucial for developers to build an algorithm to simulate an engaging randomization method. This research proposed the use of Linear Congruential Generator (LCG) based pseudo-random numbers generator in an RPG. To test the performance of the proposed method, we developed our own game titled "Rudantara", a third person roguelike game. The game employed LCG-based random generators to randomized loot drops, statistics, and upgrades. The objective of this research is to measure the performance of the proposed randomization method in our game and analyze its impact on gameplay, fairness and player satisfaction.

关键词： Human computer interaction Technological innovation Role playing games Games Generators information technology

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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Real-Time Deepfake Detection for AI-Generated Arabic Speech

Real-Time Deepfake Detection for AI-Generated Arabic Speech

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Rewaa Awaad Abdelrahman Hedya Mariam Fadel Ahmed B. Zaky Department of Computer Science and Information Technology Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science and Information Systems Minia University Minya Egypt Faculty of Engineering at Shoubra Benha University Benha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC model can accurately differentiate between real and synthetic speech samples in various languages and its generalization capability across different linguistic contexts. Experimental evaluations show that the model achieves accuracies of 92% using Ensemble Learning (XGBoost and LightGBM), 93% with Meta Learning, and 90% with Neural Networks. Assessments encompass scenarios with real Arabic and English speech not included in training datasets, as well as different speakers across languages. The study contributes insights into the robustness and practical application of RVC for speech authentication.

关键词： Training Metalearning Deepfakes Neural networks Speech recognition Linguistics Robustness Real-time systems Speech synthesis Ensemble learning

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Hybrid approach for next basket recommendation system

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International Journal of information technology (Singapore) 2023年第3期15卷 1733-1740页

作者： Yannam, V Ramanjaneyulu Kumar, Jitendra Vankayala, Tejaswini Babu, Korra Sathya Department of Computer Science and Engineering National Institute of Technology Rourkela Sector-1 Odisha Rourkela 769008 India Department Indian Institute of Information Technology Design and Manufacturing Kurnool Jagannathagattu Andhra pradesh Kurnool 518007 India

The recommendation system’s task is to predict what a user would like to buy in his/her next shopping period given in shopping history. Many state-of-the-art recommendation systems have relied mainly on user explicit feedback to deliver recommendations to the end-users since implicit feedback does not directly reflect user feedback. A set of items (multiple) that can be recommended for an user is called next basket recommendation. Most of the next basket recommendation systems treat user sequential behavior and common interests differently. These types of systems are only capable to capture local sequential behavior between adjacent baskets. In this study, an effort was made to close the gap between the hybrid approach’s locally and globally collected item sequences. This paper proposes a hybrid recommendation system that deals with both issues, achieving better results. The model consists of an autoencoder to extract rich contextual information from the user implicit feedback and a recurrent neural network to learn a dynamic representation of the user and capture global sequential features between baskets. The proposed approach has been tested on two bench mark datasets and the results indicate that it is more effective than the state-of-the-art recommendation systems. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Autoencoders Deep learning Hybrid Recurrent neural networks

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Predictive Analysis of Cardiovascular Disease using Machine Learning Techniques 7

Predictive Analysis of Cardiovascular Disease using Machine ...

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7th International Conference on Image information Processing, ICIIP 2023

作者： Sreekruti Vijh, Surbhi Kumar, Sumit Gupta, Neetu Roy, Nihar Ranjan Amity University Department of Computer Science & Engineering Uttar Pradesh Noida India Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Amity University Department of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371406

Cardiovascular or Heart diseases encompass a range of medical condition that impact the heart and blood vessels. The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths. In the United States, cardiovascular disease is responsible for an estimated 655,000 deaths annually, making it the leading cause of death, accounting for approximately 1 in every 4 deaths. To address the need for accurate prediction of heart disease, extensive research has employed advanced machine learning models. This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms. The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers. Through the utilization of these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk. The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods. It exhibited remarkable accuracy, yielding prediction results of approximately 90.16%. This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2023 IEEE.

关键词： Predictive analytics

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Implementation of Intelligent Elderly Care system Based on Cloud Platform and NNB Algorithm 2

Implementation of Intelligent Elderly Care System Based on C...

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2nd IEEE International Conference on Control, Electronics and computer technology, ICCECT 2024

作者： Huang, Xiaoping Palaoag, Thelma Domingo Nanfang College Department of Art Design & Creative Industries Guangdong Guangzhou510970 China College of Information Technology and Computer Science University of the Cordilleras Baguio City2600 Philippines

ISBN: (纸本)9798350380958

This paper discusses how to apply advanced machine learning technology and cloud platform to the smart elderly care service system, aiming to provide more accurate and personalized service recommendations for the elderly. With the acceleration of the aging process of society, smart elderly care has become one of the key ways to solve the aging problem, and efficient service matching for the diversified and personalized needs of the elderly is particularly critical. In this study, a set of service demand prediction model based on artificial neural network (ANN) was first constructed. Through in-depth mining of multidimensional data of elderly users, a detailed portrait of users was formed, so as to accurately predict their potential service demand. However, a single ANN model may be limited by overfitting or underfitting when dealing with complex and nonlinear relationships. Therefore, AdaBoost algorithm is introduced to improve the overall model performance and is deployed on the cloud platform. A powerful and efficient Neural Network Boost Model (NNB) for intelligent decision system is constructed. As an ensemble learning method, AdaBoost can combine multiple weak learners into a strong learner, strengthen the learning ability of hard-to-learn samples by dynamically adjusting sample weights, and effectively reduce overfitting. Through seamless docking with cloud computing platform, NNB model not only makes full use of elastic computing resources of cloud services to conduct large-scale parallel training, greatly shortening model training time, but also realizes real-time processing and analysis of massive data. In the design process of personalized recommendation system for intelligent elderly care service, AdaBoost algorithm not only optimizes the training process of neural network model, but also improves the accuracy and stability of recommendation results. In summary, the implementation of the intelligent elderly care system based on cloud platform and NNB prop

关键词： Neural networks

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TSAC-DRM: A Drug Recommendation Model Based on Two-Stage Attention Mechanism and Constraint Learning

TSAC-DRM: A Drug Recommendation Model Based on Two-Stage Att...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Shaofu Lin Ziqian Qiao Jianhui Chen Faculty of Information Technology Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China Department of Informatics School of Computer Science Beijing University of Technology Beijing China School of Information Science and Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

Current drug recommendation systems primarily rely on data-driven approaches, which often overlook the importance of incorporating prior knowledge and capturing temporal dependencies in patient histories. To address these limitations, we propose TSAC-DRM, an innovative drug recommendation model that integrates a two-stage attention mechanism and constraint learning. The two-stage attention mechanism, adapted from the Crossformer, focuses on learning patient historical records and exploring implicit temporal dependencies. This allows the model to better understand the patient's condition and provide more accurate recommendations. Furthermore, TSAC-DRM formalizes drug contraindication relationships as constraints in the loss function, balancing the benefits of drugs for diseases and the risks of contraindications among comorbidities. By integrating prior knowledge through constraint learning, the model ensures the safety and rationality of the recommendations. Experiments conducted on the MIMIC-III dataset demonstrate the superiority of TSAC-DRM compared to traditional recommendation models. The results highlight the advantages of combining the two-stage attention mechanism and constraint learning in drug recommendation tasks, offering new perspectives for developing intelligent decision support systems in healthcare.

关键词： Drugs Adaptation models Technological innovation Attention mechanisms MIMICs Medical services Natural language processing Safety Recommender systems Diseases

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Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis

Implementation of Audio Signals Denoising for Perfect Speech...

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2023 International Conference on science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Julius, Olaniyan Obagbuwa, Ibidun. C. Adebiyi, Ayodele A. Michael, Esiefarienrhe B. College of Pure and Applied Sciences Landmark University Department of Computer Science Kwara-State Omu-Aran Nigeria School of Natural and Applied Sciences Sol Plaatje University Department of Computer Science & Information Technology Kimberley South Africa North-West University Department of Computer Science Mafikeng South Africa

ISBN: (纸本)9798350324785

The accuracy of any speech translation system essentially depends on the quality of the audio signal inputted into it. Many researchers have worked on different approaches in an attempt to reduce the level of noise in audio signals. Such approaches, among others, include Wavelet, Fourier Transform (FT), and deep learning. These algorithms worked well on noisy speech to a certain degree, but their degree of accuracy is not sufficient enough for speech-to-speech (S2S) translation because the presence of just a little noise in the signal can alter the semantic representation of the underlying language. Since it is nearly impossible for any of this single algorithm to produce a perfect (noiseless) signal, this paper presents a layered approach for total noise removal by stacking Principal Component Analysis (PCA) on Short Time Fourier Transform (STFT). In this approach, a band-pass channel is created using STFT, which reduces the signal noise level to the barest minimum while the residual noise is completely removed by performing PCA on the refined signals. Experimental results clearly showed that this approach almost doubles the signal-to-noise ratio (SNR) of the output signal for all the 10 audio samples being tested, thus making it relatively than the aforementioned approaches in terms of quality of outputs, and suitable for accuracy-sensitive domains such as speech-to-speech translation system development. © 2023 IEEE.

关键词： Principal component analysis

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Managing aging bridges under seismic hazards through deep reinforcement learning 12th

Managing aging bridges under seismic hazards through deep re...

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12th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2024

作者： Metwally, Z. Andriotis, C.P. Molaioni, F. Faculty of Architecture and the Build Environment Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (纸本)9781032770406

Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans. © 2024 The Author(s).

关键词： Life cycle

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