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Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep-learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease based on a deep-learning tool (DLT). The developed DLT is designed using a UNET architecture. The proposed approach is evaluated for different performance quantities on a dataset and provides promising results. An average obtained accuracy ranged between 82% and 87% with around 86% of the F-score. The obtained outcomes can open the door to applying DLMs to predict and identify ALS disease. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved exquisite results. This study proposes a new Computer-Aided Diagnosis (CAD) system to recognize and distinguish between tumors and non-tumor tissues using a newly developed middleware to integrate two deep-learning technologies to segment brain MRI scans and classify any discovered tumors. The segmentation mechanism is used to determine the shape, area, diameter, and outline of any tumors, while the classification mechanism categorizes the type of cancer as slow-growing or aggressive. The main goal is to diagnose tumors early and to support the work of physicians. The proposed system integrates a Convolutional Neural Network (CNN), VGG-19, and Long Short-Term Memory Networks (LSTMs). A middleware framework is developed to perform the integration process and allow the system to collect the required data for the classification of tumors. Numerous experiments have been conducted on different five datasets to evaluate the presented system. These experiments reveal that the system achieves 97.98% average accuracy when the segmentation and classification functions were utilized, demonstrating that the proposed system is a powerful and valuable method to diagnose BrT early using MRI images. In addition, the system can be deployed in medical facilities to support and assist physicians to provide an early diagnosis to save patients’ lives and avoid the high cost of treatments. [ABSTRACT FROM AUTHOR]

Fires can cause devastating damage to lands, properties, and humans. Many countries suffer from huge financial losses due to these fires. Therefore, there is a need to implement a practical solution to spot fires effectively and accurately. Deep-learning algorithms and artificial intelligence have been deployed recently in various fields, such as monitoring systems, economics, and detection. This paper proposes a New Light Ensemble Deep-Learning Framework (NLEDLF). This framework consists of two deeplearning technologies, which are a New Generative Adversarial Network (NGAN) and a New Convolutional Neural Network (NCNN). These two tools are incorporated into the framework along with some image preprocessing methods to detect fires using pixels. The proposed framework achieves a reasonable. [ABSTRACT FROM AUTHOR]

Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses datamining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two fromCase-1 (dataset withmany numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction. [ABSTRACT FROM AUTHOR]

A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a SupportVector Machine (SVM). DiscreteWavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial. [ABSTRACT FROM AUTHOR]

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Robots are widely used in many disciplines like manufacturing, military, and commercial. They are seen as a key to advancements in such fields. A robotic is being used to assure precision and reliability. Some bigger companies like amazon use robotics to achieve stability and speed up their productions. In real-life, robots are mostly used in environments that contain uncertainties events. Additionally, interruptions occur there. Determining several performance quantities such as the execution time, throughput, power dissipation and energy under severe circumstances is crucial in the fields where robotics are used. This paper uses the developed framework earlier on a robotic manipulator to estimate average power dissipation, also known as power consumption, and average energy. Subsequently, a comparison analysis is conducted using several free tools such as Power Gadget, HWMonitor, Sidebar Diagnostics and Open Hardware Monitor. Those free tools are chosen since they are available with no extra hidden charges, and they are easy to download and use. The aim of that analysis is to find the differences between the average estimated value using mathematical equations and the actual ones from those tools. The analysis is performed on a machine running on a Windows-based system. The results that obtained indicate that the estimated/predicted values of the dissipated power and energy are close to the actual values. Likewise, the error between those figures varies between 6% to 11%. This variation happens because the used machine is affected by its specifications, especially the number of cores of its CPU and the clock frequency. [ABSTRACT FROM AUTHOR]

Any designer wants to perform performance analysis to calculate and exploit performance, in this paper, the main aim is to compute end-to-end delay which also known as "latency" at beginning phase before start working on the implementation to ensure that the proposed design is efficient. This analysis is especially needed in real-time embedded systems and considered to be crucial. Herein, an implemented model, framework, is used to link between a Finite State Machine and a Mathematical Analytical method to compute the expected average end-to-end delay for different layers of abstractions on any network topology using a network simulator. A Hierarchical Generic FSM "HGFSM" was developed earlier to estimate the desired performance metric which is represented by the end-to-end latency in this paper. Several modifications and adjustments were made to the HGFSM and the mathematical model to be suitable for this research. Cisco Packet Tracer ver. 7.3 was used in this paper to show how that performance metric, which is latency, was computed using the implemented framework. In addition, MATLAB was used as a simulator tool along with Cisco Packet Tracer to show the effectiveness of using that framework in finding the desired metric. [ABSTRACT FROM AUTHOR]

Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters. [ABSTRACT FROM AUTHOR]