Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

Digital forensic aims to provide an assistance for making decisions about a crime by looking at a file content which usually involves image files such as GIF, BMP, JPEG and etc. JPEG is a very popular image file format. It has less structured contents than other images which makes its recovery possible in the absence of some file system metadata. However, an essential problem of which is fragmented JPEG file intertwined with non-JPEG files and/or Bifragmented in the scan area. This paper proposes RX_myKarve as a new file carving framework for solving a number of forensic recovery problems including fragmentation. The RX_myKarve basic design includes a structure-based and content-based carving approaches. It adopts machine learning and evolutionary algorithms in its main components of identification validation and reassembling. The identification and validation techniques encompass an Extreme Learning Machine (ELM) for identifying and filtering the image data in the scan area. The reassembling technique encompasses a genetic algorithm to reconstruct the data from fragmented pieces to a complete image. The main contribution of the paper lies on the reassembling of fragmented image file clusters in the scan area. The RX_myKarve is tested and evaluated by using the Digital Forensic Research Workshop (DFRWS) 2006 and 2007 forensic challenge datasets. The results show that the RX_myKarve is able to carve and fully recover all the giving cases of the DFRWS-2006 dataset, which are 19 images, and all the relevant cases of the DFRWS-2007 dataset, which are 18 images. This improvement in file carving is mostly attributed to the novel identification and reassembling techniques.

Quality management (QM) has been intensively studied from the perspective of quality management practices (QMP) and market performance in the food manufacturing industry. However, in Asian countries, studies as regards to the sizes of food manufacturing companies are being neglected. Hence, this quantitative study investigates several aspects and focuses on the extent and level of QMP implementation among small, medium, and large food manufacturing companies in Malaysia. A survey questionnaire has been used to collect the data. In general, the results show that the components and types of QMP have the highest impact on large companies and medium companies than the smallest companies. It was found that QMP significantly related to the operational performance and market performance of the food manufacturing companies in Malaysia. Moreover, the verified QMP was particularly important to improve the effectiveness of resource control of small-sized and medium-sized enterprises. The outcome of this study serves as a framework to bring an understanding of QMP and promote continuous QM improvement means to the food manufacturing industries in Malaysia and other countries of the region.

This study proposes a combination of two security techniques, namely steganography and cryptography. The proposed steganography technique is LSB because it has advantages in data embedding capacity and message imperceptibility. The LSB technique is very simple and predictable, so to improve message security HBV encryption techniques are applied. HBV encryption is applied to the message before pinning it. HBV encryption is a combination of the Beaufort algorithm and Vigenere cipher so that the message security will be stronger. By combining HBV and LSB encryption, the message can be hidden properly, and its awake. Based on the experimental results, the encryption and embedding process can run well and produce good quality encryption and stego images. Similarly, the extraction and decryption process can run perfectly.

This study examines the impact of Artificial Intelligence (AI) integrated into Information and Communications Technology (ICT) in higher education to improve teaching and learning outcomes. Through the analysis of case studies, the study delves into how AI influences teaching, learning, and overall educational processes. It examines the role of AI in higher education, its potential areas for improvement, and the challenges campus planners face in adopting AI within the ICT framework. AI integration in educational settings aims to develop adaptive learning systems tailored to individual student needs, fostering engagement and clarity in understanding. AI facilitates personalized learning paths and resources based on comprehensive data analysis, moving away from generic instructional materials. Real-time feedback from AI-powered assessment tools aids in continuous learning and lightens the workload for teachers, enhancing teaching quality. Additionally, AI-driven ICT solutions offer analytical tools for data-informed decision-making by administrators, optimizing educational outcomes. However, the integration of AI in higher education poses challenges such as ethical concerns, data privacy issues, and the necessity of faculty training. Overcoming these challenges requires careful planning, policy development, and collaboration between educators, technologists, and policymakers.