Asila AlHarmali1, Saqib Ali1, Waqas Aman1, Omar Hussain2, 1Department of Information Systems, Sultan Qaboos University, Muscat, Oman, 2School of Business, University of New South Wales, Canberra, Australia
Cyber-Physical Systems (CPS) integrate physical and embedded systems with information and communication technology systems, monitoring and controlling physical processes with minimal human intervention. The connection to information and communication technology exposes CPS to cyber risks. It is crucial to assess these risks to manage them effectively. This paper reviews scholarly contributions to cyber risk assessment for CPS, analyzing how the assessment approaches were evaluated and investigating to what extent they meet the requirements of effective risk assessment. We identify gaps limiting the effectiveness of the assessment and recommend real-time learning from cybersecurity incidents. Our review covers twentyeight papers published between 2014 and 2023, selected based on a three-step search. Our findings show that the reviewed cyber risk assessment methodologies revealed limited effectiveness due to multiple factors. These findings provide a foundation for further research to explore and address other factors impacting the quality of cyber risk assessment in CPS.
cyber risk assessment, Cyber-Physical Systems (CPS), real-time learning, cybersecurity incidents, effectiveness of risk assessment.
Connie Ni1, Jonathan Thamrun2, 1The Downtown School: A Lakeside School, 160 John St, Seattle, WA 98109, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This paper introduces "DanceWell," an app created to help dancers quickly get medical advice for injuries. The app was developed based on personal experiences and the difficulty of finding specialized care quickly and affordably. DanceWell uses artificial intelligence (AI) to analyze user input, such as symptoms and photos of injuries, to diagnose problems more accurately than traditional methods [1]. The apps main features include a photo upload tool for injury analysis and a set of yes/no questions tailored to each injury type. Experiments show that DanceWell can accurately provide medical advice, proving to be faster and more accessible than waiting for a doctors appointment. The app makes it easier for dancers to get the right care quickly, allowing them to continue training and recover faster [2]. This project shows how AI can improve healthcare by providing specific, quick, and reliable support, suggesting that such technology should be used more widely.
Dance, AI, Doctor, App.
Fenghao Liu1, Jiatong Liu2, Soroush Mirzaee3, 1Portola High School, 1001 Cadence, Irvine, CA 92618, 2University of California, Irvine, CA 92697, 3Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Overall, this app was created to solve the challenges of restaurant management [1]. We designed it to be simple and user-friendly for both managers and employees. Key features include secure authentication using Firebase, a weekly scheduling tool for managers, and an emergency contact feature to quickly find available employees [2]. Employees can manage their availability with a week-view calendar. The apps functionalities support check in check out updates for attendance and shift management, improving overall communication and coordination within the restaurant. To test its effectiveness, we surveyed managers and employees who used the app, receiving mostly positive feedback. Future updates will add direct messaging and inventory calculations within the app to incorporate more core features to the app. This app effectively boosts productivity and reduces miscommunication in restaurants, addressing common scheduling challenges and providing an solution. By focusing on enhancing communication and operational efficiency, our app aims to improve the restaurant management experience and efficiency..
Restaurant Management, Scheduling Tool, Operational Efficiency, Communication Enhancement.
Maricarl Capuno, Pollymerk Vidad and Robert de Luna, Polytechnic University of the Philippines
Addressing the challenge of the unpredictability of used car prices in the United Arab Emirates (UAE), this study develops a car price prediction model using Machine Learning Algorithms. Employing various Regression models such as Ridge Regression, Lasso Regression, Random Forest Regressor, Bagging Regressor, Decision Tree and XGBoost Regression, the researchers aim to achieve accurate pricing mechanisms providing localized relevance using the data taken from the leading online vehicle buy and sell platform in the country. The dataset incorporates various factors which include brand reputation, mileage, vehicle condition, market trends, etc. Results indicate that utilization of ensemble learning techniques such as XGBoost Regression, yields the highest accuracy. XG Boost Regression model has the highest R2 score of 0.95 whilst Bagging Regression has 0.93 and Random Forest having 0.92. Other Regression models showed average performances, Decision Tree Regression having 0.89, Lasso and Ridge, on the other hand, with 0.76. This research advances the understanding of Predictive AI in the field of pre-owned automotive retails and opens room for similar applications to other types of merchandise. The study also helps to provide the common consumers tools to allow a well-informed decision during purchases.
Pre-owned cars, Artificial intelligence (AI) algorithms, Car Price prediction, Machine learning, Lasso Regression, Random Forest Regressor, XGBoost, UAE car market.
Jailton Cruz, HemanoPerrelli de Moura and Jefferson KenedyMorais, Universidade Federal de Pernambuco - UFPE, Brazil
Nowadays, digital data is an essential enterprise asset supporting company strategies. Therefore, extracting value from these data assets is an enterprise challenge. In this sense, Data Governance is needed to manage data effectively. Master Data Management is a critical data governance process ensuring the accuracy, consistency, and accessibility of crucial data across an organization. This work applied the systematic mapping study method of systematic review criteria to obtain an overview of the existing related research literature that addresses the state-ofthe-art of Master Data Management. The research aims to identify gaps and trends in the area. To achieve the research objective, we systematically searched three databases for articles published between 2014 and 2024 to understand the field evolution comprehensively. Through this review, we aim to provide researchers with an understanding of the current state-of-the-art in Master Data Management, paving the way for further advancements in this field.
Data Governance, Master Data Management, Systematic Mapping, Literature Review.
Abhik Choudhury, IBM Corporation, Exton, PA, USA
Fuzzy logic provides a framework for dealing with uncertainty and imprecision, making it particularly useful in natural language processing (NLP) applications. A critical subset of fuzzy logic is fuzzy search, which enhances search capabilities by allowing approximate matches rather than requiring exact ones. This paper explores the integration of fuzzy search techniques within the context of wholesale pharma distribution, a field that demands high accuracy in data retrieval due to its impact on public health and safety. We investigate two distinct case studies where each demonstrates specific fuzzy search techniques tailored to address unique challenges in data retrieval. Through a Python code implementation, we illustrate how these techniques can be practically applied to improve the accuracy and efficiency of searches within large datasets common in wholesale pharma distribution environments. Our findings underscore the potential of fuzzy logic as a transformative tool for enhancing information retrieval systems. By providing practical insights and technical guidance, this research aims to empower stakeholders in the pharmaceutical industry to leverage fuzzy search techniques effectively, ultimately contributing to better data management practices and improved decision-making processes.
Fuzzy logic, Fuzzy search, NLP, Levenshtein Distance, TF-IDF Vectorization, Cosine Similarity, Wholesale Drug Distribution, Chemical Composition Search, String Matching Algorithms, Data Preprocessing, Information Retrieval, Robust Search Techniques.
Tian Zhan, Austin Amakye Ansah
Capturing aesthetically pleasing photographs can be challenging for amateur photographers due to the complexityof factors such as lighting, composition, and contrast. To address this issue, we propose a mobile applicationpowered by deep learning models and regression analysis. This application analyzes real-time image frames usingapre-trained MobileNet backbone and a custom classification layer [8]. By leveraging the Aesthetics and Attributesdatabase, the app calculates an aesthetic score for each photograph, providing instant feedback to users. Challenges encountered during development, including interfacing with machine learning models and implementingcamera functionalities, are addressed. Through experiments, we evaluate dif erent training approaches andcompare our methodology with existing research. Our solution aims to empower users to capture high-qualityphotographs by assisting them in understanding and applying fundamental principles of photography.
Machine Learning, Mobile, Tensorflow, Flutter.
Chenyu Zuo1, Jonathan Sahagun2, 1Sage Hill School, 20402 Newport Coast Dr, Newport Coast, CA 92657, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
The culinary industry, a vital component of the global economy, is increasingly challenged by job shortages and a resistance to automation. This paper addresses the growing tension between modernizing culinary practices through automation and preserving traditional cooking methods. To bridge this gap, we propose AutoCook, a semi-automation tool that seamlessly integrates with existing kitchen infrastructure, enhancing efficiency without compromising the human touch in cooking. AutoCook combines mechanical components, computational controls, and real-time data monitoring, enabling users to automate routine tasks while retaining creative control. Key challenges, such as adapting to diverse stove models, ensuring safety in hazardous kitchen environments, and managing power constraints, were effectively addressed through modular design, material innovation, and optimized power management. Experimental tests evaluated AutoCook’s response to sudden temperature fluctuations, demonstrating its capability to stabilize conditions with minimal intervention. The results highlight AutoCook as a practical, cost-effective solution for modernizing kitchens, making it an essential tool for culinary professionals and enthusiasts alike.
Culinary Automation, Semi-Automation Technology, Kitchen Efficiency, Traditional Cooking Integration.
YongQin Zeng1, Roy Chun2, 1Woodbridge high school, 2 Meadowbrook, Irvine, CA 92604, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
I made this program so I can predict and deliver ESG scores of various companies to people [1]. I conducted an experiment to test out the accuracy of my sentiment determination system for posts and got about half correct. Three important systems in my program are the posting system, the authentication system, and the user interface. I used Scraper API to retrieve the posts l need for the posting system [2]. An alternative API is SmartProxy. I used Google Firebase for my authentication system [3]. Another one I could have used is Parse. I used Flutter to build the user interface system. An alternative is React Native.
ESG Scores, Sentiment Analysis, Scraper API, Flutter UI.
Shenglin Deng, Rayyan Zaid
Sometimes people want to learn the dance of their favorite celebrity, but they often fail to notice the details when learning by themselves, my application helps users to find the details that they fail to notice and point them out, during the development process I encountered the problem of where to start analyzing the video when the length of the video is different between the user and the professional, and what to do when the computer calculates the angle of the error, I applied Machine Learning K- Means clustering and change the formula to solve the problem, he is worth using because some dancers want to improve their dancing level and ability [1].
Machine learning, Computer vision, Pose estimate, Dance Learning.
Dr.Sumeet Kaur and Prof Dr.Navneet Kaur, SGTBIM&IT, Nanak Piao, Delhi 110092,India
E-commerce, the buying and selling of goods and services over the internet, has revolutionized the way businesses operate and how consumers shop. This research paper provides a comprehensive review of the evolution and impact of e-commerce, including its historical development, current trends, challenges, and future prospects. By examining key technological advancements, consumer behaviours, market dynamics, and regulatory issues, this paper aims to shed light on the transformative power of e-commerce in the digital era. The rapid growth of e-commerce has transformed the retail landscape, influencing consumer behaviour, business models, and the global economy. This research paper aims to analyse the key factors driving the expansion of e-commerce, evaluate its impact on traditional retail businesses, and explore future trends.E-commerce, the buying and selling of goods and services over the internet, has revolutionized the way businesses operate and consumers shop. This paper will explore the history of e-commerce, its benefits and drawbacks, and its impact on business and society.
E-commerce, Online shopping, Digital marketing, Electronic data interchange (EDI), Online transactions
Ms. Marwa Salih Ramadhan and Prof. Dr. Ahmed Khorsheed, ECE Department/ College of Engineering/ University of Duhok Kurdistan Region, Iraq
Lung cancer is considered as the deadliest type of cancers worldwide . The possibilities of a patient’s survival can only be increased by early detection . It can be argued that Deep learning (DL) is an effective aid to classify lung nodules accurately based CT scan images .This paper aims to review different studies published from 2020 to 2024, that related to DL techniques to identify any malignant lung nodules in lung image through CT scan. The method of this paper is comparative analysis among different studies based on some performance matrices to identify the most accurate DL technique for detecting and classifying lung nodules. Eventually, this paper indicated that the most accurate technique for classifying is still up for debate. However, most reviewed studies showed that hybrid deep learning techniques-based CNN and RNN have proved a significant level of accuracy . Based on the comparative analysis conducted in this paper it can be seen that the GAN-RNN is the best classifier for lung nodules .It can be recommended that this classifier may be evaluated on larger data sets, to diagnose more tumor types. Furthermore, the complexity of the GAN-RNN can be reduced, and so the classifier can be implemented as a real-time clinical application.
Computed Tomography( CT), Deep Learning (DL), Lung Cancer, Classification, Accuracy.
Anthony Hsieh1, Jonathan Sahagun2, 1Legacy Magnet Academy, 15500 Legacy Rd, Tustin, CA 92782, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Waking up on time is a common challenge, often leading to significant personal and professional consequences. Traditional alarm clocks relying on auditory stimuli can be ineffective for deep sleepers or those with hearing impairments. The Water Alarm Clock addresses this issue by integrating facial recognition technology and a water spray mechanism [1]. Utilizing a Raspberry Pi, night vision camera, and water pump, the system ensures a reliable and pleasant wake-up experience [2]. Key challenges included achieving accurate facial recognition in low-light conditions and consistent water delivery, which were mitigated through advanced image processing and adjustable nozzles. Experimental results demonstrated a 90% accuracy rate in target hits and effective performance across various lighting conditions. This innovative solution offers a more dependable and personalized wake-up method, reducing the stress associated with traditional alarms. The Water Alarm Clock is a practical tool for improving wakeup routines and enhancing overall sleep quality.
Wake-up technology, Facial recognition system, Sleep improvement, Smart Alarm Systems.
Houssem Chemingui, Brest Business School, France, Centre de Recherche en Informatique, Paris 1 Panthéon Sorbonne University, France
In this vision paper, we thoroughly explore the potential of integrating artificial intelligence (AI) solutions into software product lines (SPLs) to overcome challenges like scalability and complexity. By harnessing AI's machine learning and automation capabilities, SPLs can significantly enhance feature selection, variability management, and customization. We uncover foundational concepts, expected benefits, and future research directions for AI-driven SPLs, including scalable machine learning, adaptive variability management, real-time adaptation and personalized customization. Our aim is to stimulate innovation and foster discussion in the software engineering community, driving towards more efficient, adaptable, and user-friendly software systems. The integration of AI into SPLs represents a fundamental shift in software development, promising improvements in productivity, quality, and user satisfaction.
Artificial intelligence, Software product lines, Scalability, Variability management, Customization
Ka Ling Gou1, Tongchen He2/sup>, 1Orange County School of the Arts, 1010 N Main St, Santa Ana, CA 92701, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This paper introduces the Utrip app, explaining various functions and the codes involved in this app. We solved problems like music recommendations and AI chatbox, making sure that they ran without getting errors [1]. So, the user can use it to enhance their travel experience. One of the problems that we encounter is that the GPT model we used does not naturally return a JSON format for our app to read [2]. Therefore, we need to engineer our prompt to specifically ask for a response in a designated JSON format, and parse the response string to convert it to JSON. In our experiment, we tested out the rate of error while using different prompt formats for the GPT model [3]. We found out that the prompt that specifies a JSON format in English words is the best. We also compared our app to ones made by others. Our app allows users to create their own travel plans, and the AI chatbox provides suitable suggestions based on real-time scenarios.
Trip Planning, Artificial Intelligence, Large Language Model (GPT-4), AI Chatbot.
