Abir Sarkar1, Ankita Mazumdar2 and Debjit Bhowmik2, 1Department of Civil Engineering, Swami Vivekananda University, Barrakpore, West Bengal, India, 2Department of Civil Engineering, National Institute of Technology, Silchar, Assam, India
In today’s world, the application of Artificial Intelligence (AI) has gained attention across various fields. Soft computing techniques, namely Artificial Neural Networks (ANN) and Support Vector Machines (SVM), have shown great potential due to their simplicity and ease of application across multiple fields. However, its application in the field of vibration isolation is unexplored. The screening effectiveness for both open and infilled trenches (OT and IFT) for various trench geometries was assessed in the current study using a two-dimensional finite element (FE) analysis. The study further explores the application of ANN and SVM. The Gaussian Radial Basis Function (GRBF) kernel function for the SVM model modelled the problem better than linear, Exponential Radial Basis Function (ERBF) and polynomial kernel functions. The study suggests that AI models are superior and outperformed traditional MVR models. The AI models were able to achieve coefficient of regression (R2 ) values of 0.99.
ANN, SVM, Kernel Functions, MVR, Vibration Isolation.
Darren Huang1 and Yu Sun2, 1Sage Hill School, 20402 Newport Coast Dr, Newport Beach, CA92657, 2Computer Science Department, California State Polytechnic University, Pomona, CA91768
In this project, we propose a solution to the problem of childrens exposure to harmful content on their mobiledevices . Our solution involves an application that monitors the content that children view on their devices andalerts parents or guardians if any potentially harmful content is detected. The application utilizes natural languageprocessing and machine learning technologies to analyze the content of messages and images . We faced several challenges during the development of our application, including ensuring that the applicationremained easy to use while providing accurate analysis of the content. We also had to ensure that the applicationcould run smoothly on a variety of mobile devices with dif erent operating systems . To test our application, we conducted experiments in various scenarios, such as monitoring childrens device usageduring playtime, study time, and bedtime. Our results showed that our application was highly ef ective in detectingpotentially harmful content, and it was well-received by parents who tested it. Our idea is ultimately something that people should use because it provides an additional layer of protectionforchildren against harmful content. With the increasing prevalence of mobile devices in childrens lives, there is agrowing need for solutions that help parents monitor and regulate their childrens device usage. Our applicationprovides an easy-to-use and ef ective solution to this problem.
Parental Control, Management, Artificial Intelligence, Computer Vision
Zachary Liang1, Aleksandr Smolin2, 1Margaret’s Episcopal School, 31641 La Novia Ave, San Juan Capistrano, CA 92675, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
The efficient analysis of medical data is a critical challenge faced by doctors, who often have the knowledge of what medical information they need to analyze but lack the proper coding knowledge to create a program that can accomplish what they need . This paper presents Medibot, a zero-code solution that enables doctors to use artificial intelligence (AI) to analyze medical data efficiently . Medibot is designed to be intuitive for the user, requiring no prior coding knowledge or experience. The program leverages advanced machine learning algorithms and image analysis techniques to analyze medical data and provide insights and recommendations to the user . Medibot can be used for a variety of medical applications, including the diagnosis of medical conditions and the analysis of patient data. The paper also presents the results of a pilot study conducted with doctors using Medibot, which demonstrated its effectiveness in improving the efficiency and accuracy of medical data analysis. Overall, Medibot represents a promising solution for improving healthcare outcomes using AI and data analytics .
Medical, Data analysis, Artificial Intelligence.
Dr. Gasim Alandjani,Computer Science and Engineering Department, Yanbu Industrial College, Yanbu Industrial City, Kingdom of Saudi Arabia
The integration of the Green Internet of Things (Green-IoT) and Artificial Intelligence (AI) in healthcare has the potential to revolutionize a wide range of industries, including healthcare wherein Green-IoT-connected medical devices and wearables can collect health data, which can be analyzed by AI algorithms to improve patient outcomes and support better decision-making by healthcare professionals. The convergence of AI and IoT in the field of smart health coupled with machine learning algorithms are enabling new and innovative solutions for healthcare delivery and management. AI algorithms and machine learning techniques can be used to analyze vast amounts of data generated by IoT devices, such as wearable devices, sensors, and smart home health devices, to provide insights into patient health and well-being. This research presents a review of patients’ healthcare services. Particularly, we ﬁrst give an overview of essential parameters of patients’ healthcare services through Green-IoT-enabled sensor technologies under the use case scenario. We then present a basic architecture for IoT-based healthcare systems considering key requirements in the light of the UN’s Sustainable Development Goals discussing their strengths and weaknesses in the context of the framework for patients’ healthcare services. Finally, we explored various security threats for AI-based architecture and their solutions with a comprehensive methodology to design robust and resilient patients healthcare services system needed in the context of the UN’s sustainable development goals.
Artificial Intelligence, Green IoT, Healthcare, UN’s SDGs.
Daniel Guo1, Aleksandr Smolin2, 1Marks School, 25 Marlborough Road, Southborough, MA 01772, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Computer vision models usually focus on trash detection in nature such as forests, oceans and beaches . However, highway trash is a very common yet neglected problem, dif erent from the trash found in nature, that many communities struggle with. When you drive along the highway, you can often see many concentrations of trash such as drink cans and plastic bags. This paper looks into dif erent computer vision models to best discern the diferent varieties of trash found on the highway roads . We compared two commonly used computer vision models, Yolov5 and Retinanet, to find out which one would best suit the applications of a highway trash detection model . These computer vision models were trained on a kaggle dataset of commonly found trash and their categories; we first looked at the dif erent validation steps of the models such as the environment of images and then we tested the two dif erent models on urban images of a variety of trash, allowing us to determine which model was most fit for the discovery of trash on highways. Computer vision models have traditionally been applied to detect trash in natural environments such as forests, oceans, and beaches. However, highway trash is a widespread yet often overlooked problem that many communities struggle with. Unlike natural debris, highway trash is typically composed of drink cans and plastic bags, among other items. In this paper, we investigate the use of computer vision models to identify the dif erent types of trash commonly found on highways. Specifically, we compare two widely used models, Yolov5 and Retinanet, to determine which one is better suited for developing a highway trash detection model . We trained these models on a kaggle dataset that contains various categories of commonly found trash and validated the models under diferent conditions. Subsequently, we tested both models on urban images of a variety of trash to determine their performance in identifying trash on highways. Our results suggest that Yolov5 is better suited for this task.
Yolov5, Retinanet, taco trash dataset(Kaggle), Python.
Chunlin Luo1, 2, Yuewei Zhang1, 2 and Jie Zhu1, 2, 1, 2Department of Electronic Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, 1, 2Shanghai Frontiers Science Center for Gravitational Wave Detection, 800 Dongchuan Road, Shanghai 200240, China
For the detection of gravitational wave (GW) events, a large number of precise gravitational wave templates are required, but the accuracy and effectiveness of template waveform synthesis still need to be improved. Nowadays, deep learning techniques used for waveform prediction will aid in the creation of template bank across domains. In this paper, we present a multi-step prediction method based on deep learning, which is trained by a large amount of GW waveforms originating from aligned spin binary black hole (BBH) merge, and then the trained model is able to forecast this kind of GW signals and create the GW template bank effectively. The proposed multi-step prediction method effectively utilizes the data conveyed by the waveform sequence and predicts the subsequent waveform sequences directly from the input waveform sequence, avoiding the cumulative error and facilitating easier acquisition of the desired prediction. The experiment results show that for a test set of 100,000 waveforms, the mean value of model predictions and template matching is more than 99.6%. The model also produces a satisfactory prediction when used to forecast the GW waveforms with a wider range of parameters.
Gravitational Wave, Waveform Prediction, Multi-step Prediction, Deep Learning.
Jameleh Asaad and Elena Аvksentieva, Faculty of software engineering and computer technology, ITMO University, St. Petersburg, Russia
Machine Learning (ML) environments are composed of an assortment of techniques and tools that can help solve problems in a variety of fields, including Software Engineering (SE). However, many researchers have implemented different machine learning (ML) techniques in software engineering (SE). This article surveys the use of ML techniques for the software development life cycle (SDLC). The main contributions of this survey are manifold. Firstly, it summarizes the interaction between software engineering and machine learning by analysing the existing works in this niche area. Secondly, it offers the questions and methodology of this research. Then, it provides in general the main characteristics of software engineering and machine learning, it also discusses in detail using machine learning techniques in the five stages of SDLC (requirements analysis, design, implementation, testing, and maintenance) by answering the research questions for every stage. Our conclusion is that there is a huge trend to use ML based techniques in software engineering, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on detailed practical implementations that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than isolated new ideas. Otherwise, most of these applications will be barely implemented in practice.
Machine Learning, Software Engineering, Software Development Life Cycle.
Yasir Nawaz, Shanghai Jiao Tong University
The 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode is a simple, efficient and widely used mode of operation for a block cipher. It comes with a security proof that guarantees no attacks up to the birthday bound, and have been proven secure against chosen plaintext attack up to2𝑛2⁄encrypted data blocks. We refine the 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode with small additional overhead which is known as the 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 − 𝑂𝑓𝑓𝑠𝑒𝑡 mode that is very simple, fully parallelizable and efficient compared to conventional privacy-only 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode. The 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 − 𝑂𝑓𝑓𝑠𝑒𝑡 mode take unpredictable input underlying the block cipher this lead to achieve higher resistance against differential cryptanalysis and improve the security of 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode without breaking its important advantages. We analyze the concrete security of 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 − 𝑂𝑓𝑓𝑠𝑒𝑡 mode same as provided by 𝑀. 𝐵𝑒𝑙𝑙𝑎𝑟𝑒 𝑒𝑡. 𝑎𝑙. For 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode.
Modes of operation, symmetric encryption schemes, 𝐶𝑜𝑢𝑛𝑡𝑒𝑟 mode, concrete security, pseudorandom function..
Munkhchimeg Otgonchuluun, Department of English language and culture, University of the Humanities, Ulaanbaatar city, Mongolia
Discourse (both talks and texts) - interdisciplinary approach how a language works in reality within a variety of social and life situations. Here this paper aims to explore possibilities how discourse analysis in teaching English benefits for both developing educational settings and improving related language skills, innovatively. The DA is used in making language meaningful in the wider exchange of ideas. Therefore, with the face-to-face activities among English class students, this study is engaged with multiple different facilities of social topics in practice including listening to conversations and reading passages to find out how language sounds more natural in reality. Instruction is provided earlier to the ELLs on how to select key words as the macrostructure of the whole passage or the conversation based on the own preferable English knowledge by highlighting. The activities are furthermore supported with the technique of the text mining data for enriching students’ understanding of how language works; at the end, we do summarize the results of the key words by the students and compare with the key words, imported from text mining data as text visualizations in parallel analysis.
Discourse analysis, effective teaching process, reading comprehension, extensive reading approach, communicative works.
Yuxi Sun1, Victor Phan2, 1Tarbut VTorah, 5 Federation Way, Irvine, CA, 926032Computer Science Department, California State Polytechnic University, Pomona, CA 91768
This project is an application made with the purpose of homelessness intervention . It allows for the homeless to be able to send requests to a server, and the volunteers will help fulfill them to help the homeless get their life back together. The three major components of this are the volunteer app, kiosk app, and the AI chatbot . The volunteer app is the app used by the volunteers to fulfill the requests sent by the kiosk. The kiosk app is what sends the requests to the server. The AI chatbot is a part of the kiosk app that allows the user to input questions for the bot to respond to. An experiment that was conducted was testing out the accuracy of the AI chatbot, with the result being 83%. Utilizing an app is a good idea because of how prominent cell phones are. This makes it very easy to receive and fulfill the requests as soon as possible.
Homelessness, Volunteer, AI, social network.
Benjamin Lee1, Moddwyn Andaya2, 1Rowland High School, 2000 S Otterbein Ave, Rowland Heights, CA 91748, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
Poor mental health and mental illness have become more widespread in recent years, resulting in a greater need to expand the accessibility of mental health treatment. To address this issue, this study proposes an application to provide a widely appealing and easily accessible alternative to traditional mental health treatment by implementing an artificial intelligence chatbot and gamification of self-care. The AI chatbot serves as a companion, counselor, and self-help advisor for individuals who are unable to seek professional human help, while gamification incentivizes users to maintain self-care habits which can improve both physical and mental well-being. The application uses Unity as the basis for its gameplay elements, and Inworld AI to simulate realistic and dynamic conversations through natural language processing. In an experiment involving the application, results demonstrated a greater reduction of anxiety and depression symptoms among application users than among non-users. These results highlight the positive impact of AI chatbots and gamification on overall mental health.
Artificial intelligence, Gamification, Anxiety, Depression.
Andriavelonera Anselme A.1, Rivosoaniaina Alain N2, Mahatody Thomas3, Manantsoa Victor4, 1, 2Laboratory for Mathematical and Computer Applied to the Development Systems, University of Fianarantsoa, Madagascar, 3, 4Professor on the University of Fianarantsoa, Madagascar
Information retrieval is an issue that has gained prominence since the beginning of the era of digitalization. As the volume of data becomes increasingly large, it has become essential to be able to efficiently retrieve information. As technology improves, users tend to forget the information itself, but remember the path to retrieve it. This article presents the optimization of big data loading by exploiting the power of the database management system "Elasticsearch". In this document, we observe that the traditional data processing method no longer meets the expected expectations in an environment with massive amounts of data.
Elasticsearch, optimization, Database, data processing, big data.
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