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Go to Editorial ManagerThis extensive and thorough review aims to systematically outline, clarify, and examine the numerous exploratory data analysis techniques that are employed in the intriguing and rapidly advancing domain of functional MRI research. We will particularly focus on the wide array of software applications that are instrumental in facilitating and improving these complex and often nuanced analyses. Throughout this discourse, we will meticulously assess the various strengths and limitations associated with each analytical tool, offering invaluable insights relevant to their application and overall efficacy across diverse research contexts and environments. Our aim is to create a comprehensive understanding of how these tools can be best utilized to enhance research outcomes. Through this analysis, we aspire to equip researchers with critical knowledge and essential information that could profoundly influence their methodological selections in upcoming studies. By carefully considering these factors, we hope to contribute positively to the ongoing progression of this important field of inquiry, fostering innovation and enhancing the impact of future research findings in functional MRI studies.
Accurately identifying the kind and amount of dissolved metal salts in wastewater used in oil refining processes is an iconic feature of ultraviolet and visible absorption spectroscopy. This method relies on the dissolved metal salts' ability to absorb light at certain wavelengths after reacting with it. The experiments were conducted in a lab setting with a broadband source (200-800 nm) to measure the absorbance of dissolved element salts and precisely identify the lowest concentration up to 2 ppm. A mixture of the mineral salts from oil refining operations was prepared and diluted to different concentrations using a standard solution. This allowed us to study and compare this result with the absorbance behavior of the wastewater from the Al-Dora Refinery. The two results reinforced that we can accurately estimate the detection parameters for the lowest water contamination. These materials are lead nitrate (PbNO3), phenol, calcium carbonate (CaCO3), sodium chloride (NaCl2), sulfide (SO4), and nitrate (NO3). At wavelengths of 340, 404, and 741 nm, the concentrations (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100) ppm were found, and for the concentration of 10ppm, the absorbance (0.15323, 0.15326, and 0.14685) was found, respectively. The process that has been tested with varying concentrations is considered and simulates the variation in river water concentrations caused by the river's water level and flow rate changes by the effect of rain abundance and thawing. It is fast, accurate data analysis, and a lower cost compared with the other chemical analysis and conventional methods.
In order to avoid losing sense of sight in a large portion of the working population, Diabetic Retinopathy (DR) identification during broad examination for diabetes is crucial. To prevent blindness in the future, early illness detection and measurement of disease development are essential. DR is diagnosed through medical image analysis. After the success of Deep Learning (DL) in other applications in the real world, it is considered a vital tool for upcoming health sector applications, providing solutions with accurate results for medical image analysis. This review provides a comprehensive survey of the state-of-the-art DL models for DR detection and grading using retinal fundus photography. This review thoroughly examined and summarized 81 relevant publications that were published through IEEE Xplore, Web of Science, PubMed, and Scopus between 2018 and 2023 based on the available database with binary or multiclass CNN classification models as well as the main preprocessing techniques. According to the findings of this review, transfer learning has proven to be an excellent technique for addressing the problems of limited resources for data for DR analysis. CNN models having tens or hundreds of layers are the most frequently utilized frameworks for DR classification. The most extensively utilized datasets for DR categorization are Aptos 2019 and EyePACS. Although DL has attained or surpassed human-level DR classification accuracy, there is still more work to be done in real-world clinical procedures.
The interest in the Eye-tracking technology field dramatically grew up in the last two decades for different purposes and applications like keeping the focus of where the person is looking, how his pupils and irises are reacting for a variety of actions, etc. The resulted data can deliver an extraordinary amount of information about the user when it's interlocked through advanced data analysis systems, it may show information concerned with the user’s age, gender, biometric identity, interests, etc. This paper is concerned about eye motion tracking as an unadulterated tool for different applications in any field required. The improvements in this area of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with eye-tracking techniques allow large opportunities to develop algorithms and applications. In this paper number of models were proposed based on Convolutional neural network (CNN) have been designed, and then the most powerful and accurate model was chosen. The dataset used for the training process (for 16 screen points) consists of 2800 training images and 800 test images (with an average of 175 training images and 50 test images for each spot on the screen of the 16 spots), and it can be collected by the user of any application based on this model. The highest accuracy achieved by the best model was (91.25%) and the minimum loss was (0.23%). The best model consists of (11) layers (4 convolutions, 4 Max pooling, and 3 Dense). Python 3.7 was used to implement the algorithms, KERAS framework for the deep learning algorithms, Visual studio code as an Integrated Development Environment (IDE), and Anaconda navigator for downloading the different libraries. The model was trained with data that can be gathered using cameras of laptops or PCs and without the necessity of special and expensive equipment, also It can be trained for any single eye, depending on application requirements.