Ahmad Waleed Salehi

Multiple sclerosis (MS) is a neurological condition that damages the central nervous system and is characterized by demyelination, inflammation , and axonal destruction. Early and correct MS diagnosis is essential for prompt treatment and better patient outcomes. With the help of total 3427 MRI images in the form of four different types of classes — Control Axial, Control Sagittal, MS-Axial, and MS-Sagittal. We Proposed a unique method for MS detection in this study. The pre-processing of the MRI data is the initial step of the proposed methodology. This research paper presents a comprehensive study aimed at enhancing MS classification through the augmentation of MRI data. By expanding the initial dataset of 3427 images to a robust collection of 10000 images using advanced techniques, the study offers an invaluable resource for training and evaluating MS detection algorithms, thereby fostering improved diagnostic outcomes. The VGG-16, VGG-19 and xception model are trained on the hybrid dataset, consisting of both axial and sagittal images, allowing it to capture the intricate patterns associated with MS lesions. We have obtained the highest accuracy, out of the 3 models used here, of 97%, by using the VGG-16 model.

Medical imaging has advanced to the extent that conditions including stomach ulcers, bleeding, and polyps can be diagnosed using video endoscopy. It takes a lot of time for doctors to follow up on all the images produced by medical video endoscopy. This complicates the use of labor. Automated diagnosis through computer aided approaches to analyze all the resulting images rapidly and accurately. The proposed methodology is innovative in that it seeks to create a system for diagnosing gastrointestinal disorders. The images that are sent into the deep learning networks have all been improved and have had the noise removed. The 5000 images in the Kvasir dataset are evenly split between five different categories affecting the digestive tract: dye-lifted polyps, dyed resection margins, normal cecum, polyps, and ulcerative coliti. Five finely tuned deep convolutional neural network architectures (Xception, ResNet-101, VGG-19, EfficientNetB2v3, and MobineNetV2) with weights from the ImageNet dataset. EffecientNetV2B3 outperformed and achieved accuracy of 96.0%.

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer’s disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

The Current review evaluates the human side of Industrial Revolution which is blending the physical, biological, and digital worlds, erasing the borders between technology and human. The Current article examines the potential benefits of Cloud Computing (CC) in the area of HR, and their significant advantages in various HRM processes and highlights the development and trends in the industrial revolution. The 44 articles were retrieved from free search engines like Google scholar, Proquest, Research Gate and Google from (2010-2022). The articles selected through this process were carefully analyzed to synthesize existing knowledge. The findings emphasized that cloud based HRMS offers distinctive advantages to enhance efficiency and cost-effectiveness. The review surfaces that innovative programs have disrupted traditional HR management practices by transitioning the company's segregated in-house HRMS to the cloud. The organizations; implementing and practicing such technologies have gained competitive edge over their rivals. Additionally, cloud computing facilitates efficient resource utilization, seamless scalability, elimination of hardware and software maintenance requirements, and reliable data recovery capabilities. The study suggests strategies on how HR must create a digital workplace that can innovate, collaborate, and tackle business issues.

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

This research looked into the viability of using metaheuristic algorithms in conjunction with an adaptive neurofuzzy system to predict seismicity and earthquakes. Different metaheuristic algorithms have been combined with an artificial intelligence (AI) algorithm. Subjected to seismicity is a promising factor. The new sensors have many advantages over the older, more impressive‐looking ones, including (a) a generally linear relationship between the measured values and real ground motion (described above), (b) the ability to measure three orthogonal components of ground movement in a single unit, (c) sensitivity to a very broad range of frequencies, and (d) high dynamic range, which allows for the detection of both very small and fairly large tremors. To accept the acquired results as a hybrid model of an adaptive neurofuzzy inference system with particle swarm optimization (PSO), genetic algorithm (GA), and extreme machine learning (ELM) (ANFIS‐PSO‐GA‐ELM) implemented. According to the dataset, all approaches produce excellent and realistic predictions of seismic loads; however, the method ANFIS‐PSO produces better results. All the strategies demonstrated a high level of predictability. Finally, this research urges researchers to investigate the performance of triple hybrid MT algorithms using a variety of hybrid metaheuristic methodologies, rather than the existing double hybrid MT algorithms.

The health system in today’s real world is significant but difficult and overcrowded. These hurdles can be diminished using improved health record management and blockchain technology. These technologies can handle medical data to provide security by monitoring and maintaining patient records. The processing of medical data and patient records is essential to analyze the earlier prescribed medicines and to understand the severity of diseases. Blockchain technology can improve the security, performance, and transparency of sharing the medical records of the current healthcare system. This paper proposed a novel framework for personal health record (PHR) management using IBM cloud data lake and blockchain platform for an effective healthcare management process. The problem in the blockchain-based healthcare management system can be minimized with the utilization of the proposed technique. Significantly, the traditional blockchain system usually decreases the latency. Therefore, the proposed technique focuses on improving latency and throughput. The result of the proposed system is calculated based on various matrices, such as F1 Score, Recall, and Confusion matrices. Therefore, the proposed work scored high accuracy and provided better results than existing techniques.