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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Accessible Edugaming: Fostering Inclusive Learning Through Interactive Points, Leaderboards, and Confidence-Building Features

Accessible Edugaming: Fostering Inclusive Learning Through I...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Akey Sungheetha A. Raihana Rajesh Sharma R V. Sindhu Sheila Mahapatra Department of Computer Science and Engineering Alliance University Bengaluru India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This research introduces a novel educational paradigm through the development of a MERN-based (Mon-goDB, ***, ***, ***) interactive gamification platform specifically designed f or creating inclusive learning environments. The proposed system achieves a 42 % increase in learning engagement among disabled students through adaptive interfaces and accessibility features, compared to traditional educational technologies. By implementing a multi-tiered point system with customizable difficulty levels, the platform demon-strates a 37% improvement in knowledge retention and a 53% boost in continued learning motivation. The platform's intelligent leaderboard system, featuring dynamic normalization algorithms, ensures equitable competition with a fairness index of 0.89, regardless of user ability status. Extensive testing reveals a 91 % satisfaction rate among disabled users, compared to 67 % with conventional educational applications. Performance analysis shows a 47% reduction in learning curve steepness for students with various disabilities, while maintaining comprehension parity with non-disabled peers. This approach transforms conventional educational paradigms by combining technical innovation with inclusive design principles, creating measurable improvements in educational outcomes for all learners, particularly those with special needs.

关键词： Technological innovation Adaptive systems Heuristic algorithms Refining Transforms Educational technology Knowledge discovery Performance analysis Peer-to-peer computing Testing

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Simulation modeling for energy systems analysis: a critical review

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Energy Informatics 2024年第1期7卷 75页

作者： Mundu, M.M. Nnamchi, S.N. Sempewo, J.I. Uti, Daniel Ejim Department of Electrical Telecommunication and Computer Engineering Kampala International University P.O. Box 20000 Kampala Uganda Department of Mechanical Engineering Kampala International University P.O. Box 20000 Kampala Uganda Department of Civil and Environmental Engineering College of Engineering Design Art and Technology Makerere University P.O. Box 7062 Kampala Uganda Department of Research and Publications Kampala International University P.O. Box 20000 Kampala Uganda

Introduction: Energy system simulation modeling plays an important role in understanding, analyzing, optimizing, and guiding the change to sustainable energy systems. Objectives: This review aims to examine energy system simulation modeling, emphasizing its role in analyzing and optimizing energy systems for sustainable development. Methods: The paper explores four key simulation methodologies;Agent-Based Modeling (ABM), System Dynamics (SD), Discrete-Event Simulation (DES), and Integrated Energy Models (IEMs). Practical applications of these methodologies are illustrated through specific case studies. Results: The analysis covers key components of energy systems, including generation, transmission, distribution, consumption, storage, and renewable integration. ABM models consumer behavior in renewable energy adoption, SD assesses long-term policy impacts, DES optimizes energy scheduling, and IEMs provide comprehensive sector integration. Case studies demonstrate the practical relevance and effectiveness of these models in addressing challenges such as data quality, model complexity, and validation processes. Conclusions: Simulation modeling is essential for addressing energy challenges, driving innovation, and informing policy. The review identifies critical areas for improvement, including enhancing data quality, refining modeling techniques, and strengthening validation processes. Future directions emphasize the continued importance of simulation modeling in achieving sustainable energy systems. © The Author(s) 2024.

关键词： Data quality Energy systems Optimization Renewable energy Simulation modeling Sustainability

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Addressing Challenges and Opportunities in Precision Medicine Using Deep Learning and Personalized Cancer Treatment

Addressing Challenges and Opportunities in Precision Medicin...

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Delhi Section Conference (DELCON), IEEE

作者： G S Pradeep Ghantasala Nalli Vinaya Kumari R Rajesh Sharma Pellakuri Vidyullatha Akey Sungheetha Saliha Bathool Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Malla Reddy Engineering College for Women JNTUH Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The primary aim of personalized cancer treatment is to increase effectiveness and reduce side effects by tailoring therapies based on a patient's unique genetic and molecular characteristics. This research investigates how deep learning is transforming personalized cancer care through its ability to process and analyze complex, high-dimensional data. The study explores the use of deep learning techniques in predicting tumor classifications, genetic mutations, and treatment responses by integrating clinical data, genomic profiles, and medical imaging. It reviews recent advancements in deep learning applications for cancer, such as targeted therapy through genetic profiling, tumor identification from histopathology images, and optimization of treatment protocols. Additionally, the paper discusses challenges including model transparency, clinical integration, and data security. The study demonstrates how deep learning can enhance patient outcomes and outlines future opportunities for this evolving area of cancer care.

关键词： Deep learning Privacy Accuracy Cancer treatment Genomics Real-time systems Bioinformatics Medical diagnostic imaging Cancer Tumors

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Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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A Machine Learning Perspective for Predicting Chronic Kidney Disease

A Machine Learning Perspective for Predicting Chronic Kidney...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Vanathi. D S.M. Ramesh Sudha. K Tamizharasu. K Sengottaiyan. N Kalyanasundaram P Department of CSE Nandha Engineering college Perundurai Erode Department of ECE KPR Institute of Engineering and Technology Coimbatore Department of CSE (CS) K S R College of Engineering Tiruchengode Tamilnadu Computer Science Faculty of Computer Science K S R College of Engineering Tiruchengode Tamilnadu Computer Science Faculty of Science and Technology Alliance College of Engineering and Design.Alliance University Anekal Karnataka Department of IT K S R College of Engineering Tiruchengode Tamilnadu

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Aim: The primary objective of this research is to increase accuracy in the prediction of chronic kidney disease (CKD) by using Machine Learning (ML) algorithms, including K-Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks algorithm. Methods and Materials: The proposed work included four groups. Group 1 refers to a set of four different ensemble tree ML algorithms (Random Forest, Extra Trees, AdaBoost, and XGBoost) that were used to obtain the optimal classification model to support CKD early diagnosis; Group 2 refers to the K-Nearest Neighbors algorithm, which can be used to handle missing values; Group 3 uses the Support Vector Machine algorithm to classify patients into CKD or non-CKD categories; and Group 4 refers to the Artificial Neural Networks algorithm that analyses medical data to predict CKD. Results: The proposed system improves chronic kidney disease prediction, achieving 99.2% accuracy for early detection and management on an automated platform. Conclusion: All three models, including KNN, SVM and ANN, have demonstrated their potential in accurately predicting CKD with an average accuracy of 99.2%, which performs better than four different ensemble tree ML algorithms.

关键词： Support vector machines Machine learning algorithms Accuracy Artificial neural networks Nearest neighbor methods Predictive models Prediction algorithms

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Analyzing Acute Lymphoblastic Leukemia Across Multiple Classes Using an Enhanced Deep Convolutional Neural Network on Blood Smear

Analyzing Acute Lymphoblastic Leukemia Across Multiple Class...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Pawar, Janardan Bhosle, Amol A. Gupta, Priyanka Mehta, Hemant Borate, Vishal Kisan Mali, Yogesh Kisan Indira College of Commerce & Science Department of Computer Science India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India Dr. D y Patil Institute of Technology Pune Pimpri India School of Engineering & Technology Ambi Pune India Dr D y Patil College of Engineering & Innovation Pune Talegaon India G.H Raisoni Institute of Engineering and Management Pune Wagholi India

ISBN: (纸本)9798350382693

Bone marrow cancer, commonly referred to as Acute Lymphoblastic Leukemia (ALL), is marked by the rapid proliferation of immature lymphoid cells. This condition affects both children and adults, standing as the most common type of cancer in children. Recent years have seen considerable advancements in diagnosing and treating ALL. Precise risk assessment and the formulation of an effective treatment plan hinge on a comprehensive diagnostic approach that incorporates clinical, morphological, cytogenetic, and molecular data. Nevertheless, to improve survival rates and the quality of life for individuals battling this aggressive blood cancer, further research and clinical trials are imperative to overcome challenges related to drug resistance, relapse, and long-term side effects. Accordingly, this research introduces a highly optimized deep learning model, specifically a deep optimized convolutional neural network (CNN), for the early detection and classification of ALL. The advanced CNN architecture features five convolutional blocks, including 11 convolutional layers and 4 max pooling layers. The optimization of this deep CNN model involved tuning hyper parameters such as epochs, batch size, and the use of specific optimizers, namely Adam and Adamax. Among these, the model achieved superior performance with the Adam optimizer, demonstrating high levels of accuracy and precision, at 0.93 and 0.91 respectively. © 2024 IEEE.

关键词： Lung cancer

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Data science: simulating and development of outcome based teaching method

Data science: simulating and development of outcome based te...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Sridhar, K. Shinde, Govind Chaurasia, Amrita Rani, N.R. Asha Department of Mechanical Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Management H K Institute of Management Studies and Research Maharashtra Mumbai India School of Commerce Finance and Accounting Christ Demd to Be University Ncr Campus UP Gaziabad India Department of Civil Engineering Alliance College of Engineering and Design Alliance University India

ISBN: (纸本)9798350334357

The educational researcher has a wealth of options to apply analytics to extract meaningful insights to improve teaching and learning due to the growing availability of educational data. Teaching analytics, in contrast to learning analytics, examines the quality of the classroom environment and the efficacy of the instructional methods used to improve student learning. To investigate the potential of analytics in the classroom without jeopardizing students' privacy, we suggest a data science strategy that uses simulated data using pseudocode to build test cases for educational endeavors. Hopefully, this method's findings will contribute to creating a teaching outcome model (TOM) that can be used to motivate and evaluate educator performance. In Splunk, the study's simulated methodology was carried out. Splunk is a real-time Big Data dashboard that can gather and analyze massive amounts of machine-generated data. We provide the findings as a set of visual dashboards depicting recurring themes and developments in classroom effectiveness. Our study's overarching goal is to help bolster a culture of data-informed decision-making at academic institutions by applying a scientific method to educational data. © 2023 IEEE.

关键词： Decision making

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Securing Pedestrian Crosswalks in Smart Cities: An Embedded Vision System for Pedestrian Detection and Safety Enhancement 2

Securing Pedestrian Crosswalks in Smart Cities: An Embedded ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mohan Kumar, S. Reddy, Elangovan Guruva Chandramohan, S. Prabagar, S. Latha, N. CMR University Karnataka Bangalore India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Tamil Nadu Kanchipuram India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

One of the most severe issues in the context of rapidly growing intelligent cities is the safety of pedestrians at urban crosswalks. To address this issue, the system developed an embedded vision system for real-time pedestrian detection and to enhance safety at crosswalks. A camera module captures real-time video, an image processor analyzes the captured data using a pedestrian detection algorithm, and a microcontroller manages the system's other functions. Our system can accurately identify pedestrians inside the crossing area using deep learning-based object detection models. If pedestrians are detected, an LED warning system alerts drivers to either slow down or stop, drastically decreasing the possibility of an accident. A real-time pedestrian count and any applicable safety warnings are also shown on an LCD screen. By connecting with existing systems, this smart technology may significantly improve the security of smart cities for pedestrians. Preliminary testing has demonstrated the technology has great potential for deployment in real-world urban environments. © 2023 IEEE.

关键词： computer Vision Intelligent Transportation Systems Pedestrian Detection Pedestrian Safety Smart Cities

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Performance Analysis of the Machine Learning Algorithms for the Early Detection of Breast Carcinoma

Performance Analysis of the Machine Learning Algorithms for ...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： K Vanitha S. M. Karpagavalli Mahesh T R A. Althaf Ali Sridhar T K. Anitha Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education(Deemed to be University) Coimbatore India Department of Computer Science and Engg Al Ameen Engineering College Erode Tamilnadu India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of Computer Applications Madanapalle Institute of Technology & Science Andhra Pradesh India Alliance College of Engineering and Design Alliance University Bangalore India Department of Information Technology CSI College of Engineering Ooty Tamilnadu

Breast Carcinoma, generally known as breast cancer, primarily affects women, though men can develop it as well. Because of the existence of breast tissue and exposure to female hormones, notably oestrogen, women are at a higher risk It’s critical to diagnose breast tumors early. Several techniques based on machine learning (ML were used in this study to classify breast cancer using a dataset that was made available to the public. F-score, recall, precision, preciseness, and other performance metrics were used to evaluate these ML algorithms. Previous research and experimental findings indicate that Random Forest achieved the highest accuracy, with a remarkable accuracy rate of 99.12%.

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