Analytical Studies on the Effectiveness of IoMT for Healthcare Systems

Haque, Md Alimul; Ahmad, Sultan; Sonal, Deepa; Haque, Shameemul; Kumar, Kailash; Rahman, Moidur

doi:10.24996/ijs.2023.64.9.34

Iraqi Journal of Science

2023

DOI: 10.24996/ijs.2023.64.9.34

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Analytical Studies on the Effectiveness of IoMT for Healthcare Systems

Md Alimul Haque,

Sultan Ahmad,

Deepa Sonal

et al.

Abstract: The Internet of Things (IoT) has great importance in the medical industry. The creation of intelligent sensors, intelligent machines, and superior algorithms for lightweight communication made it feasible to connect medical equipment in order to monitor biomedical signals and also to detect illnesses in patients without human intervention. This new IoT and medical equipment connection is called IoMT. This IoMT model is most adapted to this pandemic since every human being has to be interconnected and monitored… Show more

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2024

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Cited by 6 publications

References 21 publications

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Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

Ansari,

Shafi Bhat,

Dilshad Ansari

et al. 2024

Data and Metadata

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Introduction: One of the most common types of cancer and a significant contributor to the high death rates among women is breast cancer. It usually occurs in women. It is crucial to acquire a diagnosis early in order to kill cancer from becoming worse. Objective: The traditional diagnosing procedure takes more time. A fast and useful option can apply Machine Learning Technique (MLT) to identify illnesses. However new technology creates a variety of high-dimensional data kinds particularly when it comes to health or cancer data. Methods: Data classification techniques like Machine Learning are efficient. Particularly in the medical field where such techniques are often utilised to make decisions via diagnosis and analysis. Using Wisconsin Breast Cancer Dataset, the proposed research was carried out (WBCD). Some of these issues may be solved using the feature selection approach. Results: This research analyses the classification accuracy of different MLT: Logistic Regression, Support Vector Machine, and K-Nearest Neighbour. According to experiment results, SVM has the best accuracy of all algorithms, at 97.12%. Conclusion: The mentioned prediction models are based on several supervised MLT. Tenfold cross validation is applied. Additionally, author also proposed a Flow chart of breast Cancer using MLT.

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Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

Ansari,

Shafi Bhat,

Dilshad Ansari

et al. 2024

Data and Metadata

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E-waste Management Using Blockchain Technology

Kumar,

Al-Fadi Al-Sharif

2024

Data and Metadata

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Introduction; Bridging the digital divide requires the provision of affordable, fair and quality ICT. With nearly two-thirds of the world’s population still offline, there is a need to provide affordable web access for everyone. For developing countries, increasing the popularity of information and communication technology has become the most important factor in reducing poverty. The danger of electrical and electronic waste disposal contains hazardous substances, but most of the electrical and electronic equipment is still disposed of in an unhealthy environment in the field development area, affecting the level of contamination in Water, Air and Soil ultimately affecting people’s health. Eliminating E-waste responsibility and protecting the environment is a challenge for countries. Smart cities can solve environmental problems through proper waste management for improving human health, protecting water resources, and reducing pollution. Objective; In this paper, we explore how blockchain technology can help smart cities to manage E-waste by providing consistency, immutability, transparency, and accountability control in a distributed, reliable, and secure manner. We discussed the advantages of blockchain technology in various aspects of E-waste management, such as instant tracking and monitoring, E-waste disposal and E-waste management regulation compliance, proper disposal management, E-waste management, and material handling, etc. All examples of disposal services, but in our study we have found that there is no fool proof system to check the disposal of E-waste whether it has been disposed off Fully or Partially. We mainly focused on the tracking of E-waste management system for 100% safe and eco-friendly disposal from the originating point of E-waste to end disposal point of total disposal. Methods; For this, we have used machine-learning model to find the existing percentage of disposal of E-waste at the end-point which reveals that it is never 100%. And partial disposal of E-waste means we have still partial E-waste around us in different forms, which will be a threat for the society to be indulged in hazardous after effects of randomly dumping E-waste.Results; After this we have modelled a Disposal Tracking System(DTS) using blockchain technology to create an E-waste data storage as Decentralized Shareable Ledger (DSL) which records the quantity and state of E-waste data from its originating point to a different level of disposal unit and finally reflect the balance of E-waste data as NIL at the end of last disposal point.Conclusion; This system will helpful for safe and ecofriendly E-waste management and it provides complete transparency and traceability of E-waste during the life cycle of complete disposal. After implementation of this system any district or Block or Village authority can ensure to its citizens for E-waste hazards free environment and safety of natural resources

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Toward Robust Arabic Sign Language Recognition via Vision Transformers and Local Interpretable Model-agnostic Explanations Integration

Baghdadi,

AbdulAzeem,

ZainEldin

et al. 2024

Journal of Disability Research

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People with severe or substantial hearing loss find it difficult to communicate with others. Poor communication can have a significant impact on the mental health of deaf people. For individuals who are deaf or hard of hearing, sign language (SL) is the major mode of communication in their daily life. Motivated by the need to develop robust and interpretable models for the deaf community, this study presents a computer-aided diagnosis (CAD) framework for Arabic SL recognition. The interpretability and management of complicated spatial connections in SL images have been limited by prior studies using convolutional neural networks. To improve accuracy and offer model transparency, the proposed CAD framework incorporates state-of-the-art technologies such as local interpretable model-agnostic explanations (LIME) and vision transformers (ViTs). ViTs use self-attention mechanisms to interpret visuals in SL, capturing global dependencies. A stacking/voting strategy is then used to aggregate predictions from many ViT models, further optimizing the system. Two large datasets, the “ArSL21L: Arabic Sign Language Letter Dataset” and the “RGB Arabic Alphabets Sign Language Dataset,” totaling over 22,000 pictures, were used to validate this approach. Metrics including intersection over union, balanced accuracy, Youden’s index, Yule’s Q, F1 score, accuracy, precision, recall, and specificity were used to assess performance. The results show that the stacking method, which makes use of many ViT models, outperforms traditional models in every performance indicator and achieves an impressive accuracy of 99.46% and 99.88% on the ArSL21L and RGB datasets, respectively. For practical applications, interpretability is ensured by using LIME, which offers clear visual explanations for the model’s predictions.

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Analytical Studies on the Effectiveness of IoMT for Healthcare Systems

Cited by 6 publications

References 21 publications

Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

Prediction and Diagnosis of Breast Cancer using Machine Learning Techniques

E-waste Management Using Blockchain Technology

Toward Robust Arabic Sign Language Recognition via Vision Transformers and Local Interpretable Model-agnostic Explanations Integration

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