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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Naikwade, Imamhussain Sungheetha, Akey Adere, Ketema Gandasala, G.S. Pradeep Banshal, Sumit Kumar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Data Science Bangalore India Adama Science and Technology University Department of Computer Science & Engineering Adama Ethiopia

ISBN: (纸本)9798331518592

This paper examines the convergence of cloud computing, facts science, and facts engineering, providing a primer for college kids getting into those fields. The examine highlights the synergistic courting among those domains, displaying how cloud infrastructure complements collaboration and efficiency. By illuminating those interconnections, the paper presents college students with a holistic view of the current facts landscape, making ready them to leverage current equipment withinside the cloud era. © 2024 IEEE.

关键词： data science

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Evaluating the Efficacy of Latent Variables in Mitigating data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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computer Modeling in Engineering & sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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BoxCare: A Box Embedding Model for Disease Representation and Diagnosis Prediction in Healthcare data 24

BoxCare: A Box Embedding Model for Disease Representation an...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Lv, Hang Chen, Zehai Yang, Yacong Ma, Guofang Tan, Yanchao Yang, Carl College of Computer and Data Science Fuzhou University China School of Computer Science Zhejiang Gongshang University China Department of Computer Science Emory University United States

ISBN: (纸本)9798400701726

Diagnosis prediction is becoming crucial to develop healthcare plans for patients based on Electronic Health Records (EHRs). Existing works usually enhance diagnosis prediction via learning accurate disease representation, where many of them try to capture inclusive relations based on the hierarchical structures of existing disease ontologies such as those provided by ICD-9 codes. However, they overlook exclusive relations that can reflect different and complementary perspectives of the ICD-9 structures, and thus fail to accurately represent relations among diseases and ICD-9 codes. To this end, we propose to project disease embeddings and ICD-9 code embeddings into boxes, where a box is an axis-aligned hyperrectangle with a geometric region and two boxes can clearly "include" or "exclude" each other. Upon box embeddings, we further obtain patient embeddings via aggregating the disease representations for diagnosis prediction. Extensive experiments on two real-world EHR datasets show significant performance gains brought by our proposed framework, yielding average improvements of 6.04% for diagnosis prediction over state-of-the-art competitors. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Diagnosis

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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Energy Efficiency in Sustainable Software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space 13th

Cumulative Step Size Adaptation for Adaptive SEMO in Integ...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Rudolph, Günter Wagner, Markus Department of Computer Science TU Dortmund University Dortmund Germany Department of Data Science and AI Monash University Clayton Australia

ISBN: (纸本)9789819635054

Parameter control involves dynamically adjusting the parameter values of the evolutionary algorithm throughout the optimization process, including parameters like mutation rate and operator selection. Self-adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. In this article, we review the cumulative step size adaptation method originally proposed for single-objective optimization over continuous variables and recast it for deployment in multiobjective optimization over unbounded integer space. We contribute a systematic investigation of its hyperparameters which shows that while (1) the very best configurations remain problem-specific, (2) the performance of the algorithm is largely independent of the self-adaptation scheme’s parameterization and initial configuration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Integer linear programming

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthi, M. Manirajan, S.P. Prince, A. Santhosh, R. Department of Artificial Intelligence and Data Science India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption. © 2025 IEEE.

关键词： Lung cancer

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Evaluation of Educational Fund Management and Operational Cost Efficiency: Analyzing U.S. Public Schools data

Evaluation of Educational Fund Management and Operational Co...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Eleeas, Kevina Nugraha Muljana, Richie Ayuningtyas, Lintang Diah Gunawan, Alexander Agung Santoso Setiawan, Karli Eka School of Computer Science Bina Nusantara University Jakarta Data Science Program 11480 Indonesia School of Computer Science Bina Nusantara University Jakarta Computer Science Department 11480 Indonesia

ISBN: (纸本)9798350350524

This study investigates the challenges and opportunities of managing educational funds and controlling operational costs in two educational systems. It examines how factors such as school infrastructure, technology integration, teacher professional development, and parental involvement influence educational funding and quality. This study also explores the importance of equitable and efficient fund allocation, especially for remote schools that face resource constraints and development gaps. To analyze the financial trends of educational fund management, this study applies three machine learning models: decision trees, random forests, and support vector machines. The results show that the random forest model has the highest overall accuracy (90%) and the decision tree model has perfect accuracy in classifying institutions based on local income. This study identifies the most relevant features and demonstrates the effectiveness of the models in categorizing institutions and evaluating the fund management performance in the education sector. © 2024 IEEE.

关键词： Decision trees

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A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Science UET TaxilaTaxilaPakistan Department of AI and Big Data Soonchunhyang UniversityAsanKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related *** this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many *** existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of ***,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG *** proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework *** linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ECG signals heart disease concurrent learning LSTM generalized gated pooling

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of computer science & Technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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