NTU Journal of Engineering and Technology

Applications trend today on artificial intelligence (AI). The latest development in the field of machine learning (ML) comes from deep learning which is expected to cause a powerful improvement in the field of artificial intelligence. Features Extraction(FE) is very important. These properties make it possible to characterize the issue and create models that explain a system or process. A variety of image preparation techniques or data sets, Different approaches are done to obtain a feature that will be used for artificial intelligence (AI) algorithms that projects involving ML or the trendiest and most well-liked fields, including deep learning. Algorithm selection techniques are vital in academic machine-learning research. This article discusses different categorization algorithms and new efforts to increase classification accuracy.

Quantum dot solar cells are an important class of solar cells of the third generation because of their optoelectronic characteristics suitable for photovoltaic response because of quantum confinement effect. Quantum dot structured materials are characterized by having a tunable bandgap as a result of a size change in addition to create an intermediate band. Thus, the quantum dots more thoroughly absorb the sun spectrum, and as a result, the solar cell's effectiveness is increased. Quantum dot solar cells' importance lies in increasing the efficiency through an increase in the generation of electron-hole pairs as a result of a single photon being absorbed, in other words several exciton generations. In this review, the generations of solar cells, the existing types and the materials used in each generation will be discussed, as a result of a single additionally to the qualities of quantum dot solar cells and the materials of this type.

This study aims to evaluate the effect of using eco-friendly mineral admixtures, such as calcined clay (CC), silica fume (SF), and limestone (L), as partial replacements for ordinary Portland cement (OPC) in the production of high-strength mortar(HSM). The use of these materials can help reduce the environmental impact of cement production by decreasing carbon dioxide emissions and preserving natural resources. To achieve the desired strength, different mixtures were proportioned by increasing the percentage of binder and limiting the water-to-binder ratio (w/b) with the aid of superplasticizer (SP) type G. The mechanical behavior and strength of the blended cements were evaluated, and the optimal mix was determined based on the results of mechanical behavior, strength, and flowability. The results revealed that the optimal mix that gives the best mechanical behavior is the mix F32 with combination of (8% SF + 4 %L+ 13% CC) with an increase about 35 % to control mix strength. The study concludes that using CC, SF, and L as partial replacements for OPC can improve the properties of modern concrete/mortar mixes, resulting in improved durability, service-life properties, and mechanical properties. The results of this study can be utilized as a base for future studies on the same concrete mix design.

Detecting and quantifying the extent of brain tumors poses a formidable challenge in medical centers. Magnetic Resonance Imaging (MRI) has developed as a non-invasive brain cancers' primary diagnostic tool, offering the crucial advantage of avoiding ionizing radiation. Brain tumor manually segmented boundaries within 3D MRI volumes is an exceedingly time-intensive task, heavily reliant on operator expertise. Among brain tumors, gliomas stand out as the prevalent and highly malignant, significantly impacting patients' life expectancy, particularly at their highest grade. Recognizing the pressing need for a reliable, completely automatic segmentation technique to efficiently assess tumor extent, this study introduces a robust approach. A completely automated brain tumor segmentation method is proposed, leveraging U-Net-based deep convolutional networks. This approach underwent rigorous evaluation on the Multimodal Brain Tumor Image Segmentation BraTS-19 dataset a widely recognized medical image analysis dataset featuring multimodal MRI scans of brain tumors, including glioblastoma, anaplastic astrocytoma, and lower-grade glioma, coupled with corresponding manual tumor segmentations. This dataset serves as a pivotal resource for advancing automatic brain tumor segmentation techniques and assessing their performance using metrics like the Dice score, which achieved 92% for entire tumor. Cross-validation results affirm the efficiency and promise of our method in achieving accurate segmentation.

The bailey method of gradation analysis it is one of the techniques used in pavement engineering for evaluating and designing asphalt mixtures based on the particle size distribution. This paper presents a comprehensive review of research conducted on the Bailey technique, focusing on its application and impact in the field of pavement engineering. The review encompasses studies that have investigated the effectiveness of the Bailey technique as a mix design tool, a quality control tool, and a performance assessment tool. Researchers have explored its ability to optimize asphalt mixtures for desired performance characteristics, such as rutting resistance, stability, and durability. They have also examined its effectiveness in ensuring consistency and conformity to specified requirements during asphalt production. Furthermore, the review highlights the correlation between the Bailey technique's gradation data and various performance properties of asphalt mixtures. Researchers have investigated the relationship between particle size distribution and properties like rutting resistance, moisture susceptibility, drainage, and particle packing. These studies have provided valuable insights into the influence of gradation on the performance of asphalt pavements.

This review article aims to comprehensively explore the historical significance and modern applications of gypsum, lime mortar, and pozzolanic mortar in heritage buildings. The study will investigate their properties, benefits, and limitations in the context of preservation and restoration, considering the impact of traditional construction practices on the structural integrity and cultural value of heritage structures. By analyzing existing literature, this review aims to formulate a research plan to further enhance our understanding and utilization of these materials in conserving invaluable heritage buildings.