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Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.

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Publication Date
Sat Jan 01 2022
Journal Name
Epitoanyag - Journal Of Silicate Based And Composite Materials
Flame retardants, their beginning, types, and environmental impact: a review
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The current study introduces a novel method for calculating the stability time by a new approach based on the conversion of degradation from the conductivity curve results obtained by the conventional method. The stability time calculated by the novel method is shorter than the time measured by the conventional method. The stability time in the novel method can be calculated by the endpoint of the tangency of the conversion curve with the tangent line. This point of tangency represents the stability time, as will be explained in detail. Still, it gives a clear and accurate envisage of the dehydrochlorination behavior and can be generalized to all types of polyvinyl chloride compared to the stability time measured by conventional ones based

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Publication Date
Sun May 01 2022
Journal Name
Journal Of Engineering
The Collapsible Soil, Types, Mechanism, and identification: A Review Study
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Collapsible soil has a metastable structure that experiences a large reduction in volume or collapse when wetting. The characteristics of collapsible soil contribute to different problems for infrastructures constructed on its such as cracks and excessive settlement found in buildings, railways channels, bridges, and roads. This paper aims to provide an art review on collapse soil behavior all over the world, type of collapse soil, identification of collapse potential, and factors that affect collapsibility soil. As urban grow in several parts of the world, the collapsible soil will have more get to the water. As a result, there will be an increase in the number of wetting collapse problems, so it's very important to com

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Publication Date
Tue Jun 25 2024
Journal Name
Biomedical And Pharmacology Journal
Psidium guajava: A Review on Its Pharmacological and Phytochemical Constituents
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Psidium guajava, belonging to the Myrtaceae family, thrives in tropical and subtropical regions worldwide. This important tropical fruit finds widespread cultivation in countries like India, Indonesia, Syria, Pakistan, Bangladesh, and South America. Throughout its various parts, including fruits, leaves, and barks, guava boasts a rich reservoir of bioactive compounds that have been traditionally utilized as folkloric herbal medicines, offering numerous therapeutic applications. Within guava, an extensive array of Various compounds with antioxidative properties and phytochemical constituents are present, including essential oils, polysaccharides, minerals, vitamins, enzymes, triterpenoids, alkaloids, steroids, glycosides, tannins, fl

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Publication Date
Sun Jul 01 2018
Journal Name
2018 2nd International Conference On Imaging, Signal Processing And Communication (icispc)
Analogy-based Common-Sense Knowledge for Opinion-Target Identification and Aggregation
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The development of Web 2.0 has improved people's ability to share their opinions. These opinions serve as an important piece of knowledge for other reviewers. To figure out what the opinions is all about, an automatic system of analysis is needed. Aspect-based sentiment analysis is the most important research topic conducted to extract reviewers-opinions about certain attribute, for instance opinion-target (aspect). In aspect-based tasks, the identification of the implicit aspect such as aspects implicitly implied in a review, is the most challenging task to accomplish. However, this paper strives to identify the implicit aspects based on hierarchical algorithm incorporated with common-sense knowledge by means of dimensionality reduction.

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Publication Date
Tue Feb 14 2023
Journal Name
Journal Of Educational And Psychological Researches
Analyzing the Literary Criticism Book Questions for High School Stage According to Bloom's Classification
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Abstract

 The current research aims to identify the analysis of the questions for the book of literary criticism for the preparatory stage according to Bloom's classification. The research community consists of (34) exercises and (45) questions. The researcher used the method of analyzing questions and prepared a preliminary list that includes criteria that are supposed to measure exercises, which were selected based on Bloom's classification and the extant literature related to the topic. The scales were exposed to a jury of experts and specialists in curricula and methods of teaching the Arabic language. The scales obtained a complete agreement. Thus, it was adapted to become a reliable instrument in this

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Publication Date
Thu Aug 01 2024
Journal Name
Advances In Science And Technology Research Journal
Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Tue Jun 22 2021
Journal Name
Expert Systems
Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks
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Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
The Effects of Conductance on Metastable Switches in Memristive Devices Based on Anti-Hebbian and Hebbian (AHaH) Learning Rules
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     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

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Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet atta</p> ... Show More
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