International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence最新文献

الجريمة الإلكترونية في الواقع العراقي ، دراسة وتحليل 伊拉克现实中的网络犯罪，研究与分析

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2024-08-10 DOI: 10.54938/ijemdcsai.2024.03.1.310

Osamah Nadhim Saadoon Al_Ibadi, Tariq M. Saeed, Subhi Hamadi Hamdoon

{"title":"الجريمة الإلكترونية في الواقع العراقي ، دراسة وتحليل","authors":"Osamah Nadhim Saadoon Al_Ibadi, Tariq M. Saeed, Subhi Hamadi Hamdoon","doi":"10.54938/ijemdcsai.2024.03.1.310","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2024.03.1.310","url":null,"abstract":"The Iraqi Ministry of Interior announced the number of cybercrimes and found that there is a need to reduce them while developing a special law for them because the Iraqi judiciary is still dealing with cybercrime perpetrators in accordance with the Iraqi Penal Code No. 111 of 1969. The enormous and continuous scientific progress in the field of communications in the world requires the legislator to continuously develop The Penal Code was designed to accommodate cases that are in line with technological development, including those related to social media networks. The research was based on a field study directed exclusively at university students (study sample). Students' opinions were surveyed on cybercrime topics, which were divided into personal crimes (insult, slander, fraud, and blackmail), societal crime (targeting a group, not an individual), and international crime (Iraqi security). At the end of the paper, several recommendations were included according to the analysis of the questionnaire.\u0000الخلاصة\u0000صرحت وزارة الداخلية العراقية بعدد الجرائم الإلكترونية وتبين هناك حاجة للحد منها مع وضع تشريع قانون خاص بها لأن القضاء العراقي لازال يتعامل مع مرتكبي الجريمة الإلكترونية وفق قانون العقوبات العراقي رقم 111 لسنة 1969. إن التقدم العلمي الهائل والمستمر بمجال الاتصالات في العالم يفرض على المشرع العراقي التطوير المستمر لقانون العقوبات ليستوعب الحالات التي تتماشى مع التطور التكنولوجي ومنها تلك المتعلقة بشبكات التواصل الإجتماعي.\u0000محاور الجريمة الإلكترونية التي وزعت الى جريمة شخصية (السب أو الشتم والإحتيال والإبتزاز) والجريمة المجتمعية (تستهدف فئة وليس فرد) وجريمة دولية (أمن العراق ), تم بعد ذلك عرض نتائج الإستبيان وتحليل الواقع ورفع التوصيات وفق نتائج إستبيان البحث.\u0000 ","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"8 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Smart Technologies and Mobile Applications to Enhance Shopping Mall Parking Experience 整合智能技术和移动应用，提升购物中心停车体验

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2024-03-30 DOI: 10.54938/ijemdcsai.2024.03.1.267

Luwe Li Long, Juhwan Lee, Chan Shin Yee, Eng Ee Shen, Bryan Lok Yi-Jie

引用次数: 0

Exploring the Impact of Artificial Intelligence (AI) on Learner-Instructor Interaction in Online Learning (Literature Review) 探索人工智能（AI）对在线学习中学习者与教师互动的影响（文献综述）

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-12-25 DOI: 10.54938/ijemdcsai.2023.02.1.236

Ziad H. Rakya

{"title":"Exploring the Impact of Artificial Intelligence (AI) on Learner-Instructor Interaction in Online Learning (Literature Review)","authors":"Ziad H. Rakya","doi":"10.54938/ijemdcsai.2023.02.1.236","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.236","url":null,"abstract":"The utilisation of Artificial Intelligence (AI) technology has caused remarkable changes that have taken place in the educational landscape. Through the integration of AI in online learning systems, an entirely new educational experience has been introduced, altering the ways learners and educators can interact. The emergence and evolution of AI technology have increased efficiency and productivity, enhancing teaching and learning outcomes. AI in online learning provides a distinct advantage by providing real-time feedback to learners. Traditional learning environments often suffer from the limitation of delayed feedback, impeding learners’ progress and demotivating them. However, AI-powered online learning systems excel in delivering immediate feedback to learners, enabling them to promptly identify and rectify mistakes and enhance their performance in real-time. This timely feedback fosters a supportive learning environment that encourages learners to engage in the learning process actively. The research by Vanlehn, Lynch, Schulze, Shapiro, Shelby, Taylor et al. (2005) on the Andes physics tutoring system serves as a valuable resource for understanding the lessons learned from utilising AI to support learner-instructor interaction. In contrast to traditional learning environments that offer delayed feedback, impeding the progress of learners and possibly dampening their motivation, AI-powered online learning systems provide real-time feedback. With real-time feedback, learners can instantly correct mistakes and improve their performance, thereby advancing their learning outcomes (Zhou & Mei, 2021). This literature review explores the impact of AI on learner-instructor interaction in online learning environments. The review considers how AI technology enhances and diversifies the learning process, focusing on personalised learning, real-time feedback provision, and content delivery.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cybercrime Unmasked: Investigating Cases and Digital Evidence 揭开网络犯罪的面纱：调查案件和数字证据

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-11-25 DOI: 10.54938/ijemdcsai.2023.02.1.255

Hamza Azam, Mohammad Irfan Dulloo, Muhammad Hassan Majeed, Janelle Phang Hui Wan, Lee Tong Xin, Siva Raja Sindiramutty

{"title":"Cybercrime Unmasked: Investigating Cases and Digital Evidence","authors":"Hamza Azam, Mohammad Irfan Dulloo, Muhammad Hassan Majeed, Janelle Phang Hui Wan, Lee Tong Xin, Siva Raja Sindiramutty","doi":"10.54938/ijemdcsai.2023.02.1.255","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.255","url":null,"abstract":"The advent of rapid digital technology has opened doors to a new domain for criminal activities, commonly termed as computer crimes. Stringent penalties have been instituted by various countries and institutions to combat these offenses executed through computers or networks. Central to investigating these crimes is digital evidence, pivotal in the realm of digital forensics. The digital forensics process comprises five critical phases: acquisition, preservation, analysis, reconstruction, and presentation. Its core aim is to locate and present digital evidence in court, aiding in determining the culpability of individuals involved in computer crimes. This discipline encompasses specialized fields such as data recovery, conversion, erasure, file identification, encryption, decryption, and IP address tracing to apprehend culprits. This paper conducts a thorough examination of the digital forensics stages using sample cases that elucidate five distinct computer crimes. It delves into evidence origins, collection methods, and preservation techniques utilized in the investigation. Once this meticulous process is completed, the digital forensics team compiles documented findings for presentation in a court of law.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"37 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight 分析学生学业成绩的影响因素：预测模型与启示

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-11-25 DOI: 10.54938/ijemdcsai.2023.02.1.254

Fahmida Faiza Ananna, Ruchira Nowreen, Sakhar Saad Rashid Al Jahwari, Enzo Anindya Costa, Lorita Angeline, Siva Raja Sindiramutty

{"title":"Analysing Influential Factors in Student Academic Achievement: Prediction Modelling and Insight","authors":"Fahmida Faiza Ananna, Ruchira Nowreen, Sakhar Saad Rashid Al Jahwari, Enzo Anindya Costa, Lorita Angeline, Siva Raja Sindiramutty","doi":"10.54938/ijemdcsai.2023.02.1.254","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.254","url":null,"abstract":"The fascination with understanding student academic performance has drawn widespread attention from various stakeholders, including parents, policymakers, and businesses. The 'Students Performance in Exams' dataset, available on platforms like Kaggle, stands as a treasure trove. It extends beyond test scores, encompassing diverse student attributes like ethnicity, gender, parental education, test preparation, and even lunch type. In our tech-driven age, predicting academic success has become a compelling pursuit. This study aims to delve deep into this dataset, utilizing data mining methods and robust classification algorithms like Logistic Regression and Random Forest in a Jupyter Notebook environment. Rigorous model training, testing, and fine-tuning strive for the utmost predictive accuracy. Data cleaning and preprocessing play a crucial role in establishing a reliable dataset for accurate predictions. Beyond numbers, the project emphasizes data visualization's impact, transforming raw data into comprehensible insights for effective communication. The Logistic Regression Model exhibits an impressive 87.6% accuracy, highlighting its potential in predicting academic performance. Moreover, the Random Forest Model excels with a remarkable 100% accuracy in forecasting student grades, showcasing its effectiveness in this domain.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"584 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Software Development Best Practices 安全软件开发最佳实践

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-11-25 DOI: 10.54938/ijemdcsai.2023.02.1.256

Muhammad Firdaus Fauzi, Vinod Rama Mohan, Yang Qi, Christal Chandrasegar, Saira Muzafar

{"title":"Secure Software Development Best Practices","authors":"Muhammad Firdaus Fauzi, Vinod Rama Mohan, Yang Qi, Christal Chandrasegar, Saira Muzafar","doi":"10.54938/ijemdcsai.2023.02.1.256","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.256","url":null,"abstract":"This research aims to explore optimal strategies for fortified software, enhancing the implementation of secure software development practices. Software security involves crafting and designing software that guarantees the integrity, confidentiality, and availability of its code, data, and functionalities. Often, in prioritizing functionality, security takes a back seat when organizations embark on system development. Yet, it's imperative to embed security at every phase of the Software Development Life Cycle (SDLC). Numerous methodologies and models exist for addressing software security, but only a few substantiate creating secure software applications effectively. Despite advancements, software security remains inadequately addressed, posing a challenge to integrating security protocols into the SDLC seamlessly. This review advocates specific security measures to be integrated at each SDLC level, fostering a secure SDLC. Efficient amalgamation of these processes ensures the delivery of secure software systems with minimized resource expenditure. Additionally, it highlights hurdles encountered in employing agile development methodologies for crafting secure software. These challenges stem from assessing agile ideals, principles, and security assurance procedures. These findings underscore the urgency for research facilitating safe software development, addressing barriers inhibiting its adoption. The paper serves as a valuable reference, shedding light on the significance of establishing secure software development processes.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"139 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning 利用磁共振成像和深度学习检测颅内肿瘤

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-11-20 DOI: 10.54938/ijemdcsai.2023.02.1.239

Sibtain Syed, Maqbool Khan, Rehan Ahmed, Syed Muhammad Talha

{"title":"Intracranial Tumor Detection using Magnetic Resonance Imaging and Deep Learning","authors":"Sibtain Syed, Maqbool Khan, Rehan Ahmed, Syed Muhammad Talha","doi":"10.54938/ijemdcsai.2023.02.1.239","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.239","url":null,"abstract":"An intracranial tumor is a malignant Central Nervous System (CNS) cancer that significantly contributes to global mortality. Timely prediction of these brain tumors can improve the survival rates of a patient. Magnetic Resonance Imagining (MRI) and Computed Tomography (CT) have emerged as effective non-invasive ways to extract 2D or 3D images of human internal organs, eliminating any pain or surgical procedures. However, analyzing and distinguishing the normal and abnormal tissue is a challenging task. Due to this implying Datadriven approaches, could be a pragmatic way to efficiently classify and detect regions of malignancy of a tumor. The scope of this study is to efficiently predict Brain tumors and their location by employing sophisticated optimized Deep learning models like Convolutional neural Networks (CNN) and Long-Short Term Memory (LSTM) through limited MRI images dataset of human brain. The dataset acquired from kaggle is comprised of 253 MRI images covering different angles of the human brain. The images were of different sizes and shapes, to standardize them data preprocessing simulations were made including resizing, normalizing, etc. For better categorization of model, the images were also converted to binary format (0, 1) by the One Hot Encoding technique. The ratio of training and testing data was taken as 90:10. For CNN and LSTM model, suitable hyperparameters were selected through the trial-and-error technique to ensure the best optimization of the model on the implied training data. The loss and accuracy graph depicts optimized validation and accuracy losses indicating the model to call back early stopping to save computational cost. The predicted labels for both the models and actual label were compared by a confusion matrix which showed accuracy, specificity, recall, and misclassification error to be 91.51, 92.85, 90.12, and 8.49 on average for CNN,while 95.54, 92.86, 94.2, and 5.805 for LSTM model, respectively.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovations in Security: A Study of Cloud Computing and IoT 安全创新：云计算和物联网研究

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-11-14 DOI: 10.54938/ijemdcsai.2023.02.1.252

Hamza Azam, Muhammed Ahnaf Tajwar, Sathesan Mayhialagan, Allister Jet Davis, Chan Jia Yik, Danish Ali, Siva Raja Sindiramutty

{"title":"Innovations in Security: A Study of Cloud Computing and IoT","authors":"Hamza Azam, Muhammed Ahnaf Tajwar, Sathesan Mayhialagan, Allister Jet Davis, Chan Jia Yik, Danish Ali, Siva Raja Sindiramutty","doi":"10.54938/ijemdcsai.2023.02.1.252","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.252","url":null,"abstract":"Cloud computing and IoT security are pivotal technologies that have reshaped the IT landscape. Leading tech giants like Google, Microsoft, and Amazon have harnessed their potential, continually enhancing these services. Despite their increasing user bases, these technologies face significant hurdles, with security emerging as the foremost challenge. This paper delves into the security aspects of both cloud computing and IoT services, encompassing security components, processes, threats, and real-world examples of security-related technologies. We also explore the impacts, including benefits, limitations, and future potential of cloud computing and IoT security services. In our exploration, we define cloud computing and the Internet of Things (IoT) to provide a foundational understanding of these technologies. We then investigate their real-world applications, highlighting their relevance and ubiquity in various domains. The core of this paper revolves around the discussion of security issues in cloud computing and IoT. For IoT, we dissect security components, processes, and threats, offering real-world technology examples to illustrate their significance. Similarly, for cloud computing, we delve into its security components, processes, and threats while showcasing practical applications of security technologies. The impact section assesses the benefits of cloud computing and IoT security. It also candidly addresses issues, limitations, and challenges associated with these technologies, offering insights into potential future advancements. This comprehensive study underscores the significance of security in the domains of cloud computing and IoT, offering a valuable resource for both practitioners and researchers. As these technologies continue to evolve, understanding their security implications is crucial for harnessing their full potential.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Bag – supported with mobile App 智能包-支持移动应用程序

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-09-06 DOI: 10.54938/ijemdcsai.2023.02.1.172

Khadija Abdullah Salim Al Jabri, Maryam Qasim Mohammed Al Ajmi, Maather Mansoor Abdullah Al Saidi, Vimbi Viswan

{"title":"Smart Bag – supported with mobile App","authors":"Khadija Abdullah Salim Al Jabri, Maryam Qasim Mohammed Al Ajmi, Maather Mansoor Abdullah Al Saidi, Vimbi Viswan","doi":"10.54938/ijemdcsai.2023.02.1.172","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.172","url":null,"abstract":"The uses of bags, for travellers, have been an integral part of life. People face difficulties in securing their bags against theft during travel and luggage transit. The emerging Internet of Things technologies and IoT devices like sensors, microcontrollers, GPS/GSM modules, RFID tags/ readers, fingerprint scanner and many such components helps immensely in automation and can be used to collate vast amounts of data, ranging from time- series data from sensors to spatial data. The objective of this project is to make travel bags smart by using the IoT devices and utilizing the captured data to thwart from robbery of personal belongings and to eliminate theft to great extent. The proposed smart bag with a mobile application keeps owner credentials like finger print, passcodes and other security components collected and endorsed using the IoT devices can be used to track the bag. And if the bag is stolen an emergency notifications sent to the owner mobile and used the geographical data to track down to the culprit in precision time before the stolen bag is mutilated. These and other features elaborated in this article helps the law enforcement officers track criminals easily. The smart bag will also benefit banks in secure fund transfer among automatic teller machines and business enterprises in exchanging confidential documents.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131156126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Contactless Smart WiFi-Based Application Presence or Fall Detection System: Analyzing Channel State Information (CSI) Signals 基于非接触式智能wifi的应用存在或跌落检测系统:分析信道状态信息(CSI)信号

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence Pub Date : 2023-08-28 DOI: 10.54938/ijemdcsai.2023.02.1.230

Murad M. Al-Rajab, Shadi Al Zraiqat, Kevin John, Moutasim Billah El Ayoubi, Mohammad Omar Qassem

{"title":"A Contactless Smart WiFi-Based Application Presence or Fall Detection System: Analyzing Channel State Information (CSI) Signals","authors":"Murad M. Al-Rajab, Shadi Al Zraiqat, Kevin John, Moutasim Billah El Ayoubi, Mohammad Omar Qassem","doi":"10.54938/ijemdcsai.2023.02.1.230","DOIUrl":"https://doi.org/10.54938/ijemdcsai.2023.02.1.230","url":null,"abstract":"Falls are considered to be the most common accident among people of determination and the elderly. Recently, many solutions have been proposed, whether wearable or noncontact, for people presence or falling detection (FD). These solutions can use wearable sensors to effectively monitor the health condition of elderly people at home and ensure their performance. However, all of these solutions require users to always wear specialized devices and sensors in their bodies, which limits the deployment of large-scale systems. Additionally, camera-based systems can raise privacy concerns. Recently, the non-contact Wi-Fi approach is becoming more and more popular because of its ubiquitous and non-invasiveness. In this paper, we propose a smart contactless system that uses Artificial Intelligence (AI) to analyze the Channel State Information (CSI) signals extracted from Wi-Fi signals. Our proposed application can help the people of determination and senior citizens (e.g., remote monitoring of the elderly) to be engaged all the time through closed monitoring based on ability to analyse the CSI signals extracted from Wi-Fi signals. This system can detect the presence, and falls of users without requiring them to wear any specialized devices or sensors. We believe that this application can help elderly and disabled people to remain engaged and monitored at all times, providing their communities with the means to better care for and serve them.","PeriodicalId":448083,"journal":{"name":"International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133990966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

International Journal of Emerging Multidisciplinaries: Computer Science &amp; Artificial Intelligence最新文献

International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence最新文献