Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.
Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with
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This paper proposes a collaborative system called Recycle Rewarding System (RRS), and focuses on the aspect of using information communication technology (ICT) as a tool to promote greening. The idea behind RRS is to encourage recycling collectors by paying them for earning points. In doing so, both the industries and individuals reap the economical benefits of such system. Finally, and more importantly, the system intends to achieve a green environment for the Earth. This paper discusses the design and implementation of the RRS, involves: the architectural design, selection of components, and implementation issues. Five modules are used to construct the system, namely: database, data entry, points collecting and recording, points reward
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Wireless Multimedia Sensor Networks (WMSNs) are networks of wirelessly interconnected sensor nodes equipped with multimedia devices, such as cameras and microphones. Thus a WMSN will have the capability to transmit multimedia data, such as video and audio streams, still images, and scalar data from the environment. Most applications of WMSNs require the delivery of multimedia information with a certain level of Quality of Service (QoS). This is a challenging task because multimedia applications typically produce huge volumes of data requiring high transmission rates and extensive processing; the high data transmission rate of WMSNs usually leads to congestion, which in turn reduces the Quality of Service (QoS) of multimedia appli
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The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the
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The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as
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Routing protocols are responsible for providing reliable communication between the source and destination nodes. The performance of these protocols in the ad hoc network family is influenced by several factors such as mobility model, traffic load, transmission range, and the number of mobile nodes which represents a great issue. Several simulation studies have explored routing protocol with performance parameters, but few relate to various protocols concerning routing and Quality of Service (QoS) metrics. This paper presents a simulation-based comparison of proactive, reactive, and multipath routing protocols in mobile ad hoc networks (MANETs). Specifically, the performance of AODV, DSDV, and AOMDV protocols are evaluated and analyz
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Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing appli
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Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has
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A new series of N-acyl hydrazones (4a-g) derived from indole-3-propionic acid (IPA) were synthesized. These N-acyl hydrazones were prepared by the reaction of 3-(1H-indol-3-yl) propane hydrazide and aldehyde in the existence of glacial acetic acid as a catalyst. 1HNMR and FT-IR analyses were used to identify the synthesized compounds and they were in vitro evaluated as antibacterial agents against six different types of microorganisms by using well diffusion method. All the tested N-acyl hydrazones (4a-g) displayed moderate activity against the Gram-negative E.coli, comparable to that of Amoxicillin. Some of the tested N-acyl hydrazones also exhibited intermediate activity ag
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