Browsing by Author "Khalaf, Osamah Ibrahim"
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Item A Classification Algorithm-Based Hybrid Diabetes Prediction Model(Frontiers Media S.A., 2022) Edeh, Michael Onyema; Khalaf, Osamah Ibrahim; Tavera, Carlos Andrés; Tayeb, Sofiane; Ghouali, Samir; Abdulsahib, Ghaida Muttashar; Richard Nnabu, Nneka Ernestina; Louni, AbdRahmaneDiabetes is considered to be one of the leading causes of death globally. If diabetes is not treated and detected early, it can lead to a variety of complications. The aim of this study was to develop a model that can accurately predict the likelihood of developing diabetes in patients with the greatest amount of precision. Classification algorithms are widely used in the medical field to classify data into different categories based on some criteria that are relatively restrictive to the individual classifier, Therefore, four machine learning classification algorithms, namely supervised learning algorithms (Random forest, SVM and Naïve Bayes, Decision Tree DT) and unsupervised learning algorithm (k-means), have been a technique that was utilized in this investigation to identify diabetes in its early stages. The experiments are per-formed on two databases, one extracted from the Frankfurt Hospital in Germany and the other from the database. PIMA Indian Diabetes (PIDD) provided by the UCI machine learning repository. The results obtained from the database extracted from Frankfurt Hospital, Germany, showed that the random forest algorithm outperformed with the highest accuracy of 97.6%, and the results obtained from the Pima Indian database showed that the SVM algorithm outperformed with the highest accuracy of 83.1% compared to other algorithms. The validity of these results is confirmed by the process of separating the data set into two parts: a training set and a test set, which is described below. The training set is used to develop the model's capabilities. The test set is used to put the model through its paces and determine its correctness.Item A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks(2022-09) Lilhore, Umesh Kumar; Khalaf, Osamah Ibrahim; Simaiya, Sarita; Tavera Romero, Carlos Andrés; Abdulsahib, Ghaida Muttashar; Poongodi M.; Kumar, DineshUnderwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, many sensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices is pretty challenging. Due to the constraints on energy storage of underwater wireless sensor network end devices and the complexity of repairing or recharging the device underwater, this is highly significant to strengthen the energy performance of underwater wireless sensor network. An imbalance in power consumption can cause poor performance and a limited network lifetime. To overcome these issues, we propose a depth controlled with energy-balanced routing protocol, which will be able to adjust the depth of lower energy nodes and be able to swap the lower energy nodes with higher energy nodes to ensure consistent energy utilization. The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. In the proposed energy-efficient routing protocol, an enhanced cluster head node is used to select a strategy that can analyze the remaining energy and directions of each participating node. In the simulation, the proposed model achieves 86.7% packet delivery ratio, 12.6% energy consumption, and 10.5% packet drop ratio over existing depth-based routing and energy-efficient depth-based routing methods for underwater wireless sensor network.Item Application of euler neural networks with soft computing paradigm to solve nonlinear problems arising in heat transfer(2021) Khan, Naveed Ahmad; Khalaf, Osamah Ibrahim; Romero, Carlos Andrés Tavera; Sulaiman, Muhammad; Bakar, Maharani A.In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.Item Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN(Tech Science Press, 2022) Banumathy D.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos Andrés; Raja, P. Vishnu; Sharma, Dilip KumarThe most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of breast cell malignancies formation of masses and Breast microcalcifications on the mammogram. When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network (ResNet50) for Breast Cancer Detection and Diagnosis, to obtain the Discriminative Localization, Convolutional Neural Network with Class Activation Map (CAM) has also been used to perform breast microcalcifications detection to find a specific class in the Histopathological image. The test results indicate that this method performed almost 225.15% better at determining the exact location of disease (Discriminative Localization) through breast microcalcifications images. ResNet50 seems to have the highest level of accuracy for images of Benign Tumour (BT)/Malignant Tumour (MT) cases at 97.11%. ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.Item CAD of BCD from Thermal Mammogram Images Using Machine Learning(Tech Science Press, 2022) Banumathy D.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos Andrés; Indra J.; Sharma, Dilip KumarLump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to enhanced Information and Communication Technology (ICT). For the purpose of making the whole detection process of Malignant Tumor (MT)/Benign Tumor (BT) very resourceful and time-efficient, there is now a possibility to form an automated and precise ComputerAided Diagnosis System (CADs). Several Image Pattern Recognition Techniques were used to classify breast cancer using Thermal Mammograms Image Processing Techniques (TMIPT) in the present investigation. Presenting a new model to classify the BCD with the help of TMIPT, thermal imaging, and smart devices is the aim of this research article. Using well-designed experiments like Intensive Preoperative Radio Therapy (IPRT) and BCD, the implementation and valuation of a concrete application are carried out. This proposed method is for the automatic classification of TMBI of a similar standard so that the thermal camera of FLIR One Gen 3 One 3rd Generation (FLIR One Gen 3) that can be attached to the smart devices are capable of capturing BCD using Machine Learning (ML) algorithms. To imitate the behaviour of human Artificial Intelligence (AI), designing drug formulations, helping in clinical diagnosis and robotic surgery systems, finding medical statistical datasets, and decoding human diseases’ wireless network model as well as cancer are the reasons for the ML to empower the computer and robots. The outperformance of the ML models against all other classifiers and scoring impressively across heterogeneous performance metrics like 98.44% of Precision, 98.83% of Accuracy, and 100% of Recall are observed from the comparative analysis.Item Deep learning based intelligent industrial fault diagnosis model(Tech Science Press, 2022) Surendran R.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos AndresIn the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.Item Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models(Frontiers Media S.A., 2022) Kavitha C.; Mani, Vinodhini; Srividhya S.R.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos AndrésAlzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing at an alarming rate every year. In Alzheimer's disease, the brain is affected by neurodegenerative changes. As our aging population increases, more and more individuals, their families, and healthcare will experience diseases that affect memory and functioning. These effects will be profound on the social, financial, and economic fronts. In its early stages, Alzheimer's disease is hard to predict. A treatment given at an early stage of AD is more effective, and it causes fewer minor damage than a treatment done at a later stage. Several techniques such as Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Voting classifiers have been employed to identify the best parameters for Alzheimer's disease prediction. Predictions of Alzheimer's disease are based on Open Access Series of Imaging Studies (OASIS) data, and performance is measured with parameters like Precision, Recall, Accuracy, and F1-score for ML models. The proposed classification scheme can be used by clinicians to make diagnoses of these diseases. It is highly beneficial to lower annual mortality rates of Alzheimer's disease in early diagnosis with these ML algorithms. The proposed work shows better results with the best validation average accuracy of 83% on the test data of AD. This test accuracy score is significantly higher in comparison with existing works.Item Interactive middleware services for Heterogeneous systems(Tech Science Press, 2022) Raghupathy, Vasanthi; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos Andrés; Sengan, Sudhakar; Sharma, Dilip KumarComputing has become more invisible, widespread and ubiquitous since the inception of the Internet of Things (IoT) and Web of Things. Multiple devices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapid development of interactive services in Heterogeneous Systems. This resulted in the delivery of interactive services throughout Heterogeneous Environments (HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, Smart Phones, Mobile Phones, Personal Computer (PC) and Personal Digital Assistant (PDA)) are available in the market. For easy access to information and services irrespective of the device used for working and even at the drastic change of the environment, the execution of applications on a broad spectrum of computing devices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with each other should be facilitated. Using a standard interface and data format, HE must link various devices from various platforms together to communicate with each other. To aid the interactive services performed by a middleware framework that operates on Application Programming Interface (API) over HEs, this issue aims to endorse an Adaptable Service Application Programming Interface (ASAPI).Item Investigation of AlGaN Channel HEMTs on β-Ga2O3 Substrate for High-Power Electronics(MDPI, 2022) Revathy A.; Boopathi C.S.; Khalaf, Osamah Ibrahim; Romero, Carlos Andrés TaveraThe wider bandgap AlGaN (Eg > 3.4 eV) channel-based high electron mobility transistors (HEMTs) are more effective for high voltage operation. High critical electric field and high saturation velocity are the major advantages of AlGaN channel HEMTs, which push the power electronics to a greater operating regime. In this article, we present the DC characteristics of 0.8 µm gate length (LG) and 1 µm gate-drain distance (LGD) AlGaN channel-based high electron mobility transistors (HEMTs) on ultra-wide bandgap β-Ga2O3 Substrate. The β-Ga2O3 substrate is cost-effective, available in large wafer size and has low lattice mismatch (0 to 2.4%) with AlGaN alloys compared to conventional SiC and Si substrates. A physics-based numerical simulation was performed to investigate the DC characteristics of the HEMTs. The proposed HEMT exhibits sheet charge density (ns) of 1.05 × 1013 cm−2, a peak on-state drain current (IDS) of 1.35 A/mm, DC transconductance (gm) of 277 mS/mm. The ultra-wide bandgap AlGaN channel HEMT on β-Ga2O3 substrate with conventional rectangular gate structure showed 244 V off-state breakdown voltage (VBR) and field plate gate device showed 350 V. The AlGaN channel HEMTs on β-Ga2O3 substrate showed an excellent performance in ION/IOFF and VBR. The high performance of the proposed HEMTs on β-Ga2O3 substrate is suitable for future portable power converters, automotive, and avionics applications.Item QoS in FANET Business and Swarm Data(Tech Science Press, 2022) Ortiz, Jesús Hamilton; Tavera Romero, Carlos Andrés; Ahmed, Bazil Taha; Khalaf, Osamah IbrahimThis article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks (FANET). Each drone has the ability to send and receive information (like a router); and can behave as a hierarchical node whit the intregration of three protocols: Multiprotocol Label Switch (MPLS), Fast Hierarchical AD Hoc Mobile (FHAM) and Internet Protocol version 6 (IPv6), in conclusion MPLS + FHAM + IPv6. The metrics analyzed in the FANET are: Delay, jitter, throughput, lost and sent packets/received. Testing process was carried out with swarms composed of 10, 20, 30 and 40 units; In this work, the stage with 40 droneswas analyzed showing registration processes, and sentmessages sequences between different drones that were part of the same swarm. A special analysis about the traffic between drones (end-to-end) was carried out, as well as the possible security flaws in each drone and the current status and future trends in real services. Regarding future trends, in a real environment, we took as a starting point,metrics results obtained in the simulation (positive according to the obtained results). These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future. This work also shows the experience quality from the service quality metrics obtained through a mathematical model. This quality of experience model will allow us to use it objectively in the agricultural sector, which is a great interest area and is where we are working with drones. Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector, as well as the data analysis and services available to the end customer. These services available to the end customer have been classified into a basic, medium, advanced and plus level.