Journal of Intelligent Systems and Internet of Things

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2690-6791ISSN (Online) 2769-786XISSN (Print)

IOT enabled Intelligent featured imaging Bone Fractured Detection System

Anita Venugopal , Gajender Kumar , Vinod Patidar , Prolay Biswas , Mukta Patel , Chaur Singh Rajput , Aditi Sharma

In the present era, there are lots of advancements and initiatives that have been undertaken through image processing techniques and IoT (Internet of Things). Image processing has proven its valuable insights in various applications such as GIS, biomedical, security, satellite imaging, medicine, and personal image analysis. In the context of fracture detection, image improvements, feature segmentation, and feature extraction techniques are commonly implemented including in the IoT Environment. The lower long bone, hand bone, and elbow bones are the particular interest due to their high incidence of fractures. X-ray diagnosis is a common method of detecting bone fractures due to its rapid and widespread availability. X-ray imaging involves a small amount of ionizing radiation in each part of the body, which is then captured on a particular film or digital detector. X-ray images, though they may have limitations compared to other imaging modalities, provide sufficient quality for fracture detection. There are three points of motivation for this research i.e. First- ease of use of software for patients and reduce the time for doctors and patients by screening out straight forward, Second- to decrease human mistakes that can also occur from manually inspecting a massive dataset of X-ray images to become aware of fractured sections of bones in hospitals, third- use of IoT infrastructure to collecting images of X-Rays and performing processing on received data by which we can send some accurate information back to the patients. The research aims to develop an automated environment i.e IoT emulation Framework consisting of image pre-processing such as attainment of images, pre-post-processing, segment methods, feature extraction, fracture detection, and visualization. Feature Extraction algorithm includes, CLAHE object with the preferred clip limit 2.0, CLAHE to the grayscale image, Gaussian blur to overcome more noise, Canny side detection, Hough Transform for line detection, and the gradient magnitude to acquire binary edges varied out through IoT. The framework utilizes the Canny edge detection methodology and Sobel operator for image segmentation. In this heat maps of images are also observed, which provide accurate information from bone images through IoT. The proposed system illustrates extreme accuracy and effectiveness, as proved by the results acquired from numerous experiments. The automated labeling and detection of bone fractures through photo processing by way of IoT offer great potential for fast and correct diagnosis, contributing to successful treatment outcomes.

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Vol. 9 Issue. 2 PP. 08-22, (2023)

An Intelligent Multi-Criteria Decision-Making Model for selecting an optimal location for a data center: Case Study in Egypt

Alber S. Aziz , Moahmed Emad , Mahmoud Ismail , Heba Rashad , Ahmed M. Ali , Ahmed Abdelhafeez , Shimaa S. Mohamed

For businesses that depend on reliable and secure IT systems, choosing the best location for a data center is of paramount importance. Data center accessibility, operational efficiency, cost, and security are all affected by their physical location. The procedure entails considering a wide range of elements to guarantee that the final site meets the needs of the business. This paper investigated the multi-criteria decision-making (MCDM) model to select the best data center location based on a set of criteria. The MCDM method is integrated with the single-valued neutrosophic set (SVNS) to deal with vague and inaccurate information. A neutrosophic set with truth, indeterminacy, and falsity membership functions all in the range [0, 1] is called a SVNS. This paper used SVNS with three MCDM methods such as entropy, TOPSIS, and MABAC techniques. The entropy technique is used to compute the weights of criteria, then the TOPSIS and MABAC methods are used to rank the locations. The case study is investigated in Egypt. This paper used ten criteria and eight alternatives.

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Vol. 9 Issue. 2 PP. 23-35, (2023)

Intelligent Wireless Sensor Networks for Healthcare: Bridging Biomedical Clothing to the IoT Future

Sajjad Ali Ettyem , Ibrahem Ahmed , Wasan Saad Ahmed , Naseer Ali Hussien , Maryam Ghassan Majeed , Korhan Cengiz , Narjes Benameur

The science of achieving a healthy mind, body, and spirit through objectives and activities is known as personal health (PH). We must be aware of our mental, bodily, and social well-being. The term "hygiene" refers to a wide variety of healthy behaviours. Individuals' healthcare costs and quality of life increased by avoiding or reducing the long-term effects of the disease through knowledge and skills. Biomedical apparel includes sutures, vascular grafts, and biodegradable clothes (BC). Biomedical clothing is anything implanted or incorporated into the human body and used near tissue, blood, or cells. Quick, dependable, and energy-efficient connectivity between wireless sensor networks is necessary (WSNs). Physical layers, media access control, networking layers, and control requirements must be co-designed. For those with lesser means, health insurance will increase in cost. There are difficulties with privacy and cyber security, a higher chance of malpractice claims, and increased time and financial expenditures for doctors and patients. In this study, wireless sensor networks-based personal health biomedical clothing (PH-BC-WSN) was utilized to increase access to high-quality healthcare, increase food production through precision agriculture, and raise the standard of human resources. More effective healthcare and medical asset monitoring systems can be developed thanks to the Internet of Things. Eavesdropping on medical data, modification, fabrication of warnings, denial of service, user tracking and location, physical interference with equipment, and electromagnetic threats were extensively discussed. The article gives several instances of current technology, discusses design challenges including energy efficiency, security, and scalability, provides various demonstrations of current technology, and provides a complete analysis of all the advantages and disadvantages. Despite their many benefits, body sensor networks have several significant obstacles and unresolved research problems, which are described along with some potential answers. As a result, the experimental findings demonstrate that PH-BC-WSN enhances accuracy and reduces response time in inpatient health monitoring.

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Vol. 9 Issue. 2 PP. 36-50, (2023)

Developing a Risk Management System with an Optimistic Predictive Approach and Business Decision-Making

Mustafa Nazar Dawood , Mohammed Ayad Alkhafaji , Ahmed Hussian , Hussein Alaa Diame , Naseer Ali Hussien , Sahar Yassine , Venkatesan Rajinikanth

Risk Management is an important task that helps to monitor the business application to eliminate the political, financial, cultural, and social consequences. The organization's risk decision is affected by several characteristics, such as lack of accountability and risk decision-making. The difficulties are resolved by applying the Machine-Learning related Business Decision Making Approach (ML-BDMA). The created framework helps to reduce the difficulties in decision-making while managing the organization's risk. The Business Decision Making process works along with the Optimistic Predictive Techniques (OPT) that are used to identify the risk which leads to attaining the business objective. This process categorizes the risk according to the qualitative characteristics of business data. The system's effectiveness was evaluated using the experimental result in which the system ensures a 98.93% performance rate, 92.25% reliability rate, 93.47% authenticity rate, 91.11% risk management rate, and 97.77% development rate while making a business decision.

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Vol. 9 Issue. 2 PP. 51-64, (2023)

A Framework for Strategic Planning Adaptation in Smart Cities through Recurrent Neural Networks

Marwa S. Mahdi Hussin , Mohammed Brayyich , Mustafa Al-Tahee , Tamarah A. Diame , Sajad Ali Zearah , Marwan Qaid Mohammed , Salem Saleh Bafjaish

In the Smart city environment, sustainable sewage and wastewater management planning plays a crucial role in industry development. Wastewater management is a serious issue with inadequate treatment, which reduces the smart city efficiency. Therefore, this research work concentrates on creating the Strategic Planning Adaption framework (SP-AF) using the Recurrent Neural Networks (RNN). This framework intends to manage the sewage and wastewater in smart cities. The sewage-related information is continuously collected by a recurrent network that identifies and tracks the wastewater and sewage in the smart city. The SP-AF framework analyses sustainable planning and managing wastewater by understanding the waste origin. In addition, the framework has been generated by understanding the wastewater knowledge, and the required actions are carried out. Then the effectiveness of the wastewater management system efficiency is compared with the existing approaches.

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Vol. 9 Issue. 2 PP. 65-77, (2023)

Anomaly Detection in Complex Power Grid using Organic Combination of Various Deep Learning (OC-VDL)

Tamarah Alaa Diame , Kadim A. Jabbar , Ahmed Taha , Naseer Ali Hussien , Sura Rahim Alatba , Mohammed Nasser Al-Mhiqani , Venkatesan Rajinikanth

The development of power industries creates impacts on the intelligent power grids. The power grids are more valuable for transmitting information over the network. Several intermediate activities influence the networks, which are interrupted by traffic, creating network security issues. Therefore, the threats highly influence power grids, and the number of attacks also increased gradually. Several conceptual approaches are introduced to overcome the security issues; however, computation complexity is still a significant problem while detecting network anomalies. This research problem is overcome by applying the Organic Combination of Various Deep Learning (OC-VDL) approach. The introduced method observes the industry standards with the help of the Innovative Blockchain Network (IBN). During this process, IBN observes the infrastructure using the communication protocol and Manufacturing Internet of Things (IoT). The collected information is processed with the help of the Intense Autoencoder Classifier Model (IACM), which manages bilateral traffic control and helps predict abnormal activities. The effective prediction of network traffic minimizes the intermediate activities and improves the overall security up to 98.8% accuracy.

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Vol. 9 Issue. 2 PP. 78-92, (2023)