Vaishali H. Kamble, Manisha P. Dale, Pravin B. Chopade, Priyanka S. Tondewad
{"title":"Evolution in Children Fingerprint Recognition Approaches: A Review","authors":"Vaishali H. Kamble, Manisha P. Dale, Pravin B. Chopade, Priyanka S. Tondewad","doi":"10.12785/ijcds/160173","DOIUrl":"https://doi.org/10.12785/ijcds/160173","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"15 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920551","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}
{"title":"AES 32: An FPGA implementation of Lightweight-AES for IoT Devices","authors":"Sumit Singh Dhanda, Brahmjit Singh, P. Jindal","doi":"10.12785/ijcds/160167","DOIUrl":"https://doi.org/10.12785/ijcds/160167","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920772","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}
{"title":"Microservices for Asset Tracking Based on Indoor Positioning System","authors":"Dondi Sasmita, Gede Putra Kusuma","doi":"10.12785/ijcds/160162","DOIUrl":"https://doi.org/10.12785/ijcds/160162","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920786","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}
Elia Ahidi Elisante Lukwaro, K. Kalegele, Devotha G. Nyambo
{"title":"A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data","authors":"Elia Ahidi Elisante Lukwaro, K. Kalegele, Devotha G. Nyambo","doi":"10.12785/ijcds/160170","DOIUrl":"https://doi.org/10.12785/ijcds/160170","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921142","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}
{"title":"Classification of Tuberculosis Based on Chest X-Ray Images for Imbalance Data using SMOTE","authors":"Muhammad Fadhlullah Kh.TQ, W. Wahyono","doi":"10.12785/ijcds/160171","DOIUrl":"https://doi.org/10.12785/ijcds/160171","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921039","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}
Hayder Qasim Flayyih, Jumana Waleed, Amer M. Ibrahim
{"title":"IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review","authors":"Hayder Qasim Flayyih, Jumana Waleed, Amer M. Ibrahim","doi":"10.12785/ijcds/160159","DOIUrl":"https://doi.org/10.12785/ijcds/160159","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919994","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}
{"title":"QR Shield: A Dual Machine Learning Approach Towards Securing QR Codes","authors":"Hissah Almousa, Arwa Almarzoqi, Alaa Alassaf, Ghady Alrasheed, Suliman A. Alsuhibany","doi":"10.12785/ijcds/160164","DOIUrl":"https://doi.org/10.12785/ijcds/160164","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920268","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}
Shilpi Garg, Rajesh Kumar Kaushal, Naveen Kumar, E. Boonchieng
{"title":"Exploring Research Challenges of Blockchain and Supporting Technology with Potential Solution in Healthcare","authors":"Shilpi Garg, Rajesh Kumar Kaushal, Naveen Kumar, E. Boonchieng","doi":"10.12785/ijcds/160138","DOIUrl":"https://doi.org/10.12785/ijcds/160138","url":null,"abstract":"","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698568","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}
Rafly Arief Kanza, M. Udin, Harun Al Rasyid, S. Sukaridhoto
: Rising healthcare challenges, particularly undiagnosed heart disease due to subtle symptoms and limited access to diagnostics, necessitate innovative solutions. This study introduces an innovative Internet of Things (IoT)-based system for early detection, leveraging the strengths of both fuzzy logic and machine learning. By analyzing patient-specific data such as heart rate, oxygen saturation, galvanic skin response, and body temperature, our system utilizes fuzzy logic to evaluate potential disease symptoms, enabling self-diagnosis under medical supervision. This personalized approach enables individuals to monitor their health and seek prompt medical attention as needed. Additionally, we train multiple machine learning algorithms (Decision Tree, KNN, SVM, Random Forest, Logistic Regression) on the well-established Cleveland heart disease dataset. Among these, Random Forest achieved the highest accuracy (82.6%), precision (81.5%), recall (83.7%), and F1-Score (82.5%), showcasing its e ff ectiveness in predicting cardiovascular disease. This unique blend of fuzzy logic for personalized symptom assessment and machine learning for CVD prediction presents a new method for early diagnosis. While promising, further validation through large-scale clinical trials is essential. Ultimately, this system underscores the significance of integrating AI with medical expertise for optimal patient care, providing a potential pathway to improved health outcomes and enhanced accessibility to early detection of cardiovascular disease.
:日益严峻的医疗保健挑战，尤其是由于症状不明显和诊断途径有限而导致的心脏病未确诊，需要创新的解决方案。本研究利用模糊逻辑和机器学习的优势，介绍了一种基于物联网（IoT）的创新型早期检测系统。通过分析心率、血氧饱和度、皮肤电反应和体温等患者特定数据，我们的系统利用模糊逻辑来评估潜在的疾病症状，从而在医疗监督下实现自我诊断。这种个性化方法使个人能够监测自己的健康状况，并在需要时及时就医。此外，我们还在成熟的克利夫兰心脏病数据集上训练了多种机器学习算法（决策树、KNN、SVM、随机森林、逻辑回归）。其中，随机森林算法的准确率（82.6%）、精确率（81.5%）、召回率（83.7%）和 F1 分数（82.5%）均为最高，显示了其在预测心血管疾病方面的有效性。这种将用于个性化症状评估的模糊逻辑与用于心血管疾病预测的机器学习相结合的独特方法，为早期诊断提供了一种新方法。虽然前景广阔，但通过大规模临床试验进行进一步验证至关重要。最终，该系统强调了将人工智能与医学专业知识相结合以优化患者护理的重要性，为改善健康状况和提高心血管疾病早期检测的可及性提供了一条潜在的途径。
{"title":"Efficient Early Detection of Patient Diagnosis and Cardiovascular Disease using an IoT System with Machine Learning and Fuzzy Logic","authors":"Rafly Arief Kanza, M. Udin, Harun Al Rasyid, S. Sukaridhoto","doi":"10.12785/ijcds/160115","DOIUrl":"https://doi.org/10.12785/ijcds/160115","url":null,"abstract":": Rising healthcare challenges, particularly undiagnosed heart disease due to subtle symptoms and limited access to diagnostics, necessitate innovative solutions. This study introduces an innovative Internet of Things (IoT)-based system for early detection, leveraging the strengths of both fuzzy logic and machine learning. By analyzing patient-specific data such as heart rate, oxygen saturation, galvanic skin response, and body temperature, our system utilizes fuzzy logic to evaluate potential disease symptoms, enabling self-diagnosis under medical supervision. This personalized approach enables individuals to monitor their health and seek prompt medical attention as needed. Additionally, we train multiple machine learning algorithms (Decision Tree, KNN, SVM, Random Forest, Logistic Regression) on the well-established Cleveland heart disease dataset. Among these, Random Forest achieved the highest accuracy (82.6%), precision (81.5%), recall (83.7%), and F1-Score (82.5%), showcasing its e ff ectiveness in predicting cardiovascular disease. This unique blend of fuzzy logic for personalized symptom assessment and machine learning for CVD prediction presents a new method for early diagnosis. While promising, further validation through large-scale clinical trials is essential. Ultimately, this system underscores the significance of integrating AI with medical expertise for optimal patient care, providing a potential pathway to improved health outcomes and enhanced accessibility to early detection of cardiovascular disease.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"56 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710842","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}
: Investigating digital evidence by gathering, examining, and maintaining evidence that was stored in smartphones has attracted tremendous attention and become a key part of digital forensics. The mobile forensics process aims to recover digital evidence from a mobile device in a way that will preserve the evidence in a forensically sound condition. This evidence might be used to prove being a cybercriminal or a cybercrime victim. To do this, the mobile forensics process lifecycle must establish clear guidelines for safely capturing, isolating, transporting, storing, and proving digital evidence originating from mobile devices. There are unique aspects of the mobile forensics procedure that must be considered. It is imperative to adhere to proper techniques and norms for the testing of mobile devices to produce reliable results. In this paper, we develop a novel methodology for the mobile forensics process model lifecycle named Mobile Forensics Investigation Process Framework (MFIPF) which encompasses all the necessary stages and data sources used to construct the crime case. The developed framework contributes to identifying common concepts of mobile forensics through the development of the mobile forensics model that simplifies the examination process and enables forensics teams to capture and reuse specialized forensic knowledge. Furthermore, the paper provides a list of the most commonly used forensics tools and where we can use them in our proposed mobile forensic process model.
{"title":"A Novel Framework for Mobile Forensics Investigation Process","authors":"Mohammed Moreb, Saeed Salah, Belal Amro","doi":"10.12785/ijcds/160110","DOIUrl":"https://doi.org/10.12785/ijcds/160110","url":null,"abstract":": Investigating digital evidence by gathering, examining, and maintaining evidence that was stored in smartphones has attracted tremendous attention and become a key part of digital forensics. The mobile forensics process aims to recover digital evidence from a mobile device in a way that will preserve the evidence in a forensically sound condition. This evidence might be used to prove being a cybercriminal or a cybercrime victim. To do this, the mobile forensics process lifecycle must establish clear guidelines for safely capturing, isolating, transporting, storing, and proving digital evidence originating from mobile devices. There are unique aspects of the mobile forensics procedure that must be considered. It is imperative to adhere to proper techniques and norms for the testing of mobile devices to produce reliable results. In this paper, we develop a novel methodology for the mobile forensics process model lifecycle named Mobile Forensics Investigation Process Framework (MFIPF) which encompasses all the necessary stages and data sources used to construct the crime case. The developed framework contributes to identifying common concepts of mobile forensics through the development of the mobile forensics model that simplifies the examination process and enables forensics teams to capture and reuse specialized forensic knowledge. Furthermore, the paper provides a list of the most commonly used forensics tools and where we can use them in our proposed mobile forensic process model.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"230 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141692612","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}
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