areej fatima

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Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

IEEE Access

Identifying human diseases remains a difficult process, even in the age of advanced information t... more Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The Google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0. INDEX TERMS IoMT, transfer learning, deep machine learning, histopathology, image processing, lung cancer.

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An Ontology Model Approach for Cloud Service Level Agreement (SLAs)

2022 International Conference on Business Analytics for Technology and Security (ICBATS)

Service Level Agreement (SLA) is established between customers and service providers. Reading SLA... more Service Level Agreement (SLA) is established between customers and service providers. Reading SLA in a documented format is a very time consuming task, therefore a platform is required to simplify the reading task, and to integrate various SLA agreements. Semantic Web Ontology model can greatly simplify the problem of understanding SLAs, and comparing SLAs of different Service Providers. Therefore, a Semantic Ontology Model is proposed in the paper. The research work provides an Ontology based platform to help the customer and service provider to better understand the SLA. The integrated Ontology models facilitate users to understand various aspects of SLA agreement. Valuable time of users can be saved by using Integrated Ontology. Models because users don’t need to read whole SLA documents, and users can also get information according to their requirements.

Data and Machine Learning Fusion Architecture for Cardiovascular Disease Prediction

Computers, Materials & Continua

Heart disease, which is also known as cardiovascular disease, includes various conditions that af... more Heart disease, which is also known as cardiovascular disease, includes various conditions that affect the heart and has been considered a major cause of death over the past decades. Accurate and timely detection of heart disease is the single key factor for appropriate investigation, treatment, and prescription of medication. Emerging technologies such as fog, cloud, and mobile computing provide substantial support for the diagnosis and prediction of fatal diseases such as diabetes, cancer, and cardiovascular disease. Cloud computing provides a cost-efficient infrastructure for data processing, storage, and retrieval, with much of the extant research recommending machine learning (ML) algorithms for generating models for sample data. ML is considered best suited to explore hidden patterns, which is ultimately helpful for analysis and prediction. Accordingly, this study combines cloud computing with ML, collecting datasets from different geographical areas and applying fusion techniques to maintain data accuracy and consistency for the ML algorithms. Our recommended model considered three ML techniques: Artificial Neural Network, Decision Tree, and Naïve Bayes. Real-time patient data were extracted using the fuzzy-based model stored in the cloud.

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Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Intelligent Automation & Soft Computing

Complexity Analysis of DE based CE-UD MIMO System

2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2019

Today’s world is demanding high data volume communication without any major interruption in the c... more Today’s world is demanding high data volume communication without any major interruption in the channel. To fulfill the user’s requirements Multiple Input Multiple Output (MIMO) technology seems promising which enhance the capacity of the channel. A channel is a medium through which signal travels. MIMO sends data on multiple paths using multiple antennas. At up-link receiver end of the MIMO system, to restore data special algorithms are used. Space and Time complexity of those algorithms play an important role in these systems to increase the performance. Time Complexity plays an important role when both variations give the same Bit Error Rate (BER). This paper will discuss the Time and Space complexity of Differential Evolutionary (DE) based CE-UD MIMO system with Channel Estimation and User Detection. These analyses are verified mathematically as well as on simulations using MatLab 2017.

MapReduce based intelligent model for intrusion detection using machine learning technique

Journal of King Saud University - Computer and Information Sciences, 2021

Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning

IEEE Access, 2021

Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase t... more Shill Bidding (SB) occurs when the fake bidders are introduced by the seller's side to increase the final price. SB is a crime committed during the e-Auction, and it is pretty difficult to detect because of its normal bidding behavior. The bidder gets a lot of loss because he pays extra money, and the sellers benefit from shill bidding, so this article proposed a fusion base model. This proposed model is split into two parts training and validation, into 70 and 30 percent. This model has been divided into three sub-modules; the first module, two machine learning algorithms named Support vector machine (SVM), and Artificial neural network (ANN) trained parallel on the same dataset and predicting the bidding fraud. The prediction of these models becomes the input of the fuzzy-based fussed module, and fuzzy decide the actual output based on SVM and ANN predictions. On every bid, it predicts whether the fraud is committed or not. If the bidding behavior is normal, continue the bidding; otherwise, cancel the bid and block the user. The prediction accuracy of the proposed fussed machine learning approach is 99.63%. Simulation results have shown that the proposed fussed machine learning approach gives more attractive results than state-of-the-art published methods. INDEX TERMS Shill bidding, e-auction fraud, online fraud detection, deep learning model. I. INTRODUCTION Virtual Marketplace hosted on the internet is known as the E-auction. It is the process of buying and selling items through online platforms. The bidder bids the item, and the highest bidder is the winner of the item. At the beginning of the auction, bidding starts from the lowest price to a higher price depending upon the buyer's interest. The history of an auction is found in about 500 B.C when the women and the slaves were sold. In those ages, it was legal by law. In the United States, the auction was started to sell the estates, farms, and slaves, with the growth of technology, the auction was started from the computers, fax, smartphone, and many online platforms, e.g., eBay is the first online auction website started in 1995 in the United States. It is The associate editor coordinating the review of this manuscript and approving it for publication was Shenghong Li.

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Performance comparison of partitioned primary hybrid excited linear flux switching machine

Mechanics Based Design of Structures and Machines, 2020

To avoid the problem of crowded primary and rising temperature of permanent magnet (PM), the desi... more To avoid the problem of crowded primary and rising temperature of permanent magnet (PM), the design of partitioned primary hybrid excited linear flux switching machine (PP-HLFSM) positions the armature, and field excitation coil winding on separate part of the mover. This article examines the performance analysis of different structures of unaligned structure of PP-HLFSM and compared the proposed designs by reducing the usage of PM to half in terms of volume from 25,920 mm 3 to 12,960 mm 3 , for both concentrated and toroidal winding structure leading toward cost effectiveness. This article scales down the cost of proposed designs for elevator long stroke application. Based on finite element analysis (FEA) and boundary element analysis, the electromagnetic performance of the machine is studied in detail. Single variable optimization is implemented to optimize both the proposed structures. Analysis includes flux linkages, flux strengthening, average force characteristics, force-velocity and power-velocity curves, copper losses, iron losses, efficiency, thrust force density and power density, and demagnetization analysis. Comparison of conventional structure with proposed designs are made in terms of flux, thrust force, losses, efficiency, power density, demagnetization of PMs, and thermal analysis, along with 3D FEA analysis. Finally, the proposed structures are analytically validated.

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Novel Modular Rotor Single Phase Wound Field Flux Switching Machine

2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), 2018

In recent years, numerous topologies of single phase and three phase Field Excited Flux-Switching... more In recent years, numerous topologies of single phase and three phase Field Excited Flux-Switching Machine (FEFSM) have been developed for several application such as flux weakening capability and high speed because of robust structure of rotor. However, conventional 8S-4P sub-part rotor design has higher iron losses in stator and rotor core. Overlapped winding configuration between Field Excitation coil (FEC) and Armature coil of conventional design has a problem of high copper consumption. This paper presents novel topology of single phase modular rotor field excited FSM with 8s-6p configuration, which enable non-overlap arrangement between armature coil and FEC winding that facilitates devaluation in the copper losses. The proposed rotor design acquires reduced iron losses comparatively to conventional design. The electromagnetic performance of conventional sub-part rotor design and proposed novel-modular rotor design is analyzed by 3D-Finite Element Analysis (3D-FEA), includes flux linkage, flux strength, back-EMF, cogging torque, torque characteristics and iron losses.

Intelligent identification of Acute kidney injury empowered with Heterogeneous Mamdani Fuzzy Inference System

International Journal of Advance Research, Ideas and Innovations in Technology, 2020

In this article, a new Heterogeneous-Layered Mamdani Fuzzy Inference System (HL-MFIS) is proposed... more In this article, a new Heterogeneous-Layered Mamdani Fuzzy Inference System (HL-MFIS) is proposed to detect the Acute Kidney Injury. The proposed computerized system Detect of AKI Using Heterogeneous Mamdani Fuzzy Inference System (DAKI-HL-MFIS) Expert System, can detect the Acute Kidney Injury or No-AKI. The Expert System has two input variables at layer-I and seven input variables at layers-II. At layer-I input, variables are Creatinine and BUN that detects the output condition of a Kidney to be Normal, or Acute Kidney Injury. The further input variables at layer-II are Glomerular filtration rate, urine Albumin, sodium, potassium, chloride, calcium, and phosphorus that determine the output condition of Kidneys like Acute kidney Injury and other reasons that arise due to enzyme vaccination or due to past Kidney Injury. The overall accuracy of the DAKI-HL-MFIS Expert system is 90.5%.

An Optimal Utilization of Cloud Resources using Adaptive Back Propagation Neural Network and Multi-Level Priority Queue Scheduling

ISC Int. J. Inf. Secur., 2019

With the innovation of cloud computing industry lots of services were provided based on different... more With the innovation of cloud computing industry lots of services were provided based on different deployment criteria. Nowadays everyone tries to remain connected and demand maximum utilization of resources with minimum timeand effort. Thus, making it an important challenge in cloud computing for optimum utilization of resources. To overcome this issue, many techniques have been proposed shill no comprehensive results have been achieved. Cloud Computing offers elastic and scalable resource sharing services by using resource management. In this article, a hybrid approach has been proposed with an objective to achieve the maximum resource utilization. In this proposed method, adaptive back propagation neural network and multi-level priority-based scheduling are being carried out for optimum resource utilization. This hybrid technique will improve the utilization of resources in cloud computing. This shows result in simulation-based on the form of MSE and Regression with job dataset, o...

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An IoMT-Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique

Computational Intelligence and Neuroscience, 2021

The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast hea... more The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. The cost of related healthcare will rise as the global population of elderly people grows in parallel with an overall life expectancy that demands affordable healthcare services, solutions, and developments. IoMT may bring revolution in the medical sciences in terms of the quality of healthcare of elderly people while entangled with machine learning (ML) algorithms. The effectiveness of the smart healthcare (SHC) model to monitor elderly people was observed by performing tests on IoMT datasets. For evaluation, the precision, recall, fscore, accuracy, and ROC values are computed. The authors also compare the results of the SHC model with different conventional popular ML techniq...

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Modelling Intelligent Driving Behaviour Using Machine Learning

Computers, Materials & Continua, 2021

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

Computers, Materials & Continua, 2021

Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It ... more Diabetes mellitus, generally known as diabetes, is one of the most common diseases worldwide. It is a metabolic disease characterized by insulin de ciency, or glucose (blood sugar) levels that exceed 200 mg/dL (11.1 ml/L) for prolonged periods, and may lead to death if left uncontrolled by medication or insulin injections. Diabetes is categorized into two main types-type 1 and type 2-both of which feature glucose levels above "normal," de ned as 140 mg/dL. Diabetes is triggered by malfunction of the pancreas, which releases insulin, a natural hormone responsible for controlling glucose levels in blood cells. Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error. The primary purpose of this research study is to assess the potential role of machine learning in predicting a person's risk of developing diabetes. Historically, research has supported the use of various machine algorithms, such as naïve Bayes, decision trees, and arti cial neural networks, for early diagnosis of diabetes. However, to achieve maximum accuracy and minimal error in diagnostic predictions, there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals. Therefore, in this paper, we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

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Design and Characteristics Investigation of Partitioned Primary Hybrid Excited Linear Flux Switching Machine

2018 International Conference on Power Generation Systems and Renewable Energy Technologies (PGSRET), 2018

Partitioned-primary hybrid-excited linear flux switching machine (PP-HLFSM), 12-slots/13-pole is ... more Partitioned-primary hybrid-excited linear flux switching machine (PP-HLFSM), 12-slots/13-pole is presented in this paper and compared with the conventional design. Number of PMs is halved in the proposed design, thus reducing the cost of the machine. Unaligned structure is supported as the permanent magnets are aligned with the armature slots, improving the flux linkage and the thrust force. Field excitation coil (FEC) and armature winding configuration is positioned on the separate parts of the primary to avoid the congested situation of the primary and the heat up condition of permanent magnets. Whilst, secondary is made of iron following a simple construction. Based on the finite element analysis (FEA) and boundary element analysis (BEA), flux linkages, detent force, and average thrust force is analyzed and compared with the conventional design.

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

Computers, Materials & Continua, 2020

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not onl... more The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user's health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the

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Efficient image cipher based on the movement of king on the chessboard and chaotic system

Journal of Electronic Imaging, 2020

Abstract. A new and an efficient image cipher using chaos theory and the king chess piece is prop... more Abstract. A new and an efficient image cipher using chaos theory and the king chess piece is proposed. The given image is being treated as a large chessboard. The king scrambles the pixels of the given image through its random movement on the chessboard. Once the image is scrambled, it is further diffused through the XOR operation. Chaotic data to be used in the cipher have been generated through the Tinkerbell chaotic map. Image characteristics have been directly used to embed the plaintext sensitivity in the cipher to avoid the complicated and lengthy hash codes. The starting address of the king has also been made a key. It increases both the key space and plaintext sensitivity of the proposed image cipher. Both the simulation and comprehensive security analyses exhibit the efficiency, robustness, security, and applicability of the proposed algorithm.

Modelling, simulation, and optimization of diabetes type II prediction using deep extreme learning machine

Journal of Ambient Intelligence and Smart Environments, 2020

Diabetes is among the most common medical issues which people are facing nowadays. It may cause p... more Diabetes is among the most common medical issues which people are facing nowadays. It may cause physical incapacity or even death in some cases. It has two core types, namely type I and type II. Both types are chronic and influence the functions of the human body that regulate blood sugar. In the human body, glucose is the main element that boosts cells. However, insulin is a key that enters the cells to control blood sugar. People with diabetes type I do not have the ability to produce insulin. Whereas people with diabetes type II lack the ability to react to insulin and frequently do not make enough insulin. For adequate analysis of such a fatal disease, techniques with a minimum error rate must be utilized. In this regard, different models of artificial neural network (ANN) have been investigated in the literature to diagnose/predict the condition with a minimum error rate, however, there is a need for improvement. To further advance the accuracy, a deep extreme learning machine (DELM) based prediction model is proposed and investigated in this research. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows significant improvement in results compared to previous investigations. It is observed that during the investigation the proposed approach has the highest accuracy rate of 92.8% with 70% of training (9500 samples) and 30% of test and validation (4500 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

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An RGB Image Cipher Using Chaotic Systems, 15-Puzzle Problem and DNA Computing

IEEE Access, 2019

The application of confusion and diffusion processes on the three individual components of an RGB... more The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.

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Cloud Services Ranking by measuring Multiple Parameters using AFIS

ICST Transactions on Scalable Information Systems, 2018

Assigning a level to a number of choices is referred to as ranking. The concept of ranking is app... more Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly used these days. Similarly, in cloud standardization, ranking the web services is a principal concern, as it is a relatively new approach, assigning ranks to cloud facilities has gained significant attention from researchers across the globe. Furthermore, cloud services standardization is an important idea as it is necessary if it is required to assign ranking for cloud services. There are few limitations in cloud standardization as there is no technique to check valid services and its classifications, wherein, the standardization of cloud services will play a major role in controlling the redundancy of cloud services. In this article, a new cloud service ranking method is proposed using an Adaptive Fuzzy Inference System (AFIS).

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