Mohammed M. Al-Ani

• National University of Malaysia

Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accura...

The aim of this study is to improve the performance of detection Birds Species by proposed a hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for dimensionality reduction, and Support Vector Machines (SVM) for classification. The methodology includes preprocessing, augmentation, feature extraction, and classifi...

The prediction of proton energy shows a key part in various scientific and technological studies including particle physics, medical imaging, and radiation therapy. In the last years the regression techniques study a lot trying to discover the difficult relationships among proton properties energy, needing to be studying of additional advanced tech...

The Internet of Things (IoT) has been regarded as the most critical technology due to its resource-constrained sensors transmitted via low-power wireless technologies beneath low-power lossy networks (LLNs), where the LLN has high latency and lower throughput due to its traffic patterns. The IoT possesses low-cost and low-power sensor technology, w...

The detection of adverse drug reactions (ADRs) is an important piece of information for determining a patient's view of a single drug. This study attempts to consider and discuss this feature of drug reviews in medical opinion-mining systems. This paper discusses the literature that summarizes the background of this work. To achieve this aim, the f...

Accurately predicting student performance remains a significant challenge in the educational sector. Identifying students who need additional support early can significantly impact their academic outcomes. This study aims to develop an intelligent solution for predicting student performance using supervised machine learning algorithms. This propose...

Predicting viscosity’s nanofluids can benefit all domains, including energy, thermofluids, power systems, energy storage, materials, cooling, heating, and lubrication. The objective of this study to predict the dynamic viscosity of polyalphaolefin-hexagonal boron nitride (PAO/hBN) nanofluids using four main parameters: shear rate, shear stress, nan...

The detection of adverse drug reactions (ADRs) plays a necessary role in comprehending the safety and benefit profiles of medicines. Although spontaneous reporting stays the standard approach for ADR documents, it suffers from significant under-reporting rates and limitations in terms of treatment inspection. This study proposes an ensemble model t...

Computer aid models such as machine learning (ML) are massively observed to be successfully applied in different engineering-related domains. The current research was designed to predict the thermo-economic performances of hybrid organic Rankine plants. The XGBoost optimization algorithm was used to select the influencing parameters for the plant's...

Big data is improving the healthcare industry and creating opportunities for improved patient care, personalized medicine, and advanced research. This brief review article aims to survey the challenges and issues connected with big data in healthcare, discuss recent developments in the field, and highlight future directions for helping big data to...

Scientific manuscripts an important in publishing research findings and advancing scientific knowledge. The method of writing a manuscript can take time and be a challenge for a lot of researchers. Artificial intelligence have helped the improvement of scientific writing. ChatGPT is an AI language model developed by OpenAI. This study review aims t...

In last years, computer vision has shown important advances, mainly using the application of supervised machine learning (ML) and deep learning (DL) techniques. The objective of this review is to show a brief review of the current state of the field of supervised ML and DL techniques, especially on computer vision tasks. This study focuses on the m...

This study presents a robust hybrid model for face recognition, which synergistically integrates the VGG16 convolutional neural network (CNN) for feature extraction with an autoencoder for dimensionality reduction and representation learning. This paper proposed VGG16 and Autoencoder architecture efficiently extracts high-level features from images...

Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection...

Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning...

Skin cancer is considered one of the most fatal illnesses in the human population. Within the current healthcare system, the procedure of identifying skin cancer is time-consuming and poses a potential risk to human life if not recognized promptly. Early identification of skin cancer is imperative to maximize the likelihood of achieving complete re...

A precise prediction of student performance is an important aspect within educational institutions toimprove results and provide personalized support of students. However, the predication accuracy of studentperformance considers an open issue within education field. Therefore, this paper proposes a developed approachto identify performance of stude...

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for molecular sensing and has gained significant attention due to its high sensitivity and selectivity. SERS based on deep learning technology have been used in this study of materials, biological recognition, food safety, and intelligence. Deep learning techniques have shown tremen...

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to ca...

Sarcasm detection is considered one of the most challenging tasks in sentiment analysis and opinion mining applications in the social media. Sarcasm identification is therefore essential for a good public opinion decision. There are some studies on sarcasm detection that apply standard word2vec model and have shown great performance with word-level...

Detecting Adverse Drug Reactions (ADRs) is one of the important information for determining the view of the patient on one drug. Most studies have investigated the extraction of ADRs from social networks, in which users share their opinion on a particular medication. Some studies have used trigger terms to detect ADRs. Such studies showed remarkabl...