Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC

Enhancement of Image Transmission Using Chaotic Interleaver over Wireless Sensor Network

The wireless sensor networks different from classic wired networks, WMSN differs from other scalar network mainly nature and size of data transmitted, memory resources, and power consumption in each node for processing and transmission. The images broadcasting over wireless multimedia sensor networks that can be used in IEEE 802.15.4 (Zig-Bee) for short-range multimedia transmissions. In this paper a strong interleaver mechanism prepared to reduce or immune a burst error of network , this can be done by applying the chaotic interleaving on the pixel, bit ,and chip. The enhancement simulation for bit error rate and peak signal to noise rationnbsp by transceiver image cameraman though AWGN and Rayleigh fadingnbsp channels are displayed. While transmitting the image by 20 dB signal to noise ratio on the Rayleigh fading channel, an improvement on the peak signal to noise ratio of the received image from 25.9 dB to 78.4 dB can be observed

Impact of Delay and Queue on the Length of Left-Turn Storage at Palestine Intersections in Baghdad city, Iraq

At intersections, Left-turning vehicles seek to occupy the same physical space as close to the stop line as possible. These result in high conflicts, delays, and blockage of vehicles by turning vehicles and vice versa. The impact of the lengths of Left-turn lanes on intersection delays is considered to optimize the lengths of the Left-turn lanes. Data for traffic counts, queue lengths, and signal timing are collected from three intersections in Baghdad city in Iraq. The methodology involves the development of estimation models using traffic Simulation SIDRA INTERSECTION 8.0 and simulating various scenarios by varying traffic signal conditions to evaluate delays and queues caused by varying lengths of the Left-turn Lane. Optimal lengths are computed and compared to existing lengths in intersections. No differences in delay, queue, and lengths of the Left turn lane are found using t-test analysis for significance. Outputs from the three models were compared to the maximum observed in the field from the selected intersections. Data analysis involved determining the R2 and the standard error mean between the model output and the observed data. In general, SIDRA INTERSECTION 8.0 overestimated queue vehicles and length of storage for approaches with a high degree of saturation ratios and underestimated it for those with a high degree of saturation ratios

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Impact of Delay and Queue on the Length of Left-Turn Storage at Palestine Intersections in Baghdad city, Iraq

At intersections, Left-turning vehicles seek to occupy the same physical space as close to the stop line as possible. These result in high conflicts, delays, and blockage of vehicles by turning vehicles and vice versa. The impact of the lengths of Left-turn lanes on intersection delays is considered to optimize the lengths of the Left-turn lanes. Data for traffic counts, queue lengths, and signal timing are collected from three intersections in Baghdad city in Iraq. The methodology involves the development of estimation models using traffic Simulation SIDRA INTERSECTION 8.0 and simulating various scenarios by varying traffic signal conditions to evaluate delays and queues caused by varying lengths of the Left-turn Lane. Optimal lengths are computed and compared to existing lengths in intersections. No differences in delay, queue, and lengths of the Left turn lane are found using t-test analysis for significance. Outputs from the three models were compared to the maximum observed in the field from the selected intersections. Data analysis involved determining the R2 and the standard error mean between the model output and the observed data. In general, SIDRA INTERSECTION 8.0 overestimated queue vehicles and length of storage for approaches with a high degree of saturation ratios and underestimated it for those with a high degree of saturation ratios

A case study to examine undergraduate students’ intention to use internet of things (IoT) services in the smart classroom

Recently, the education sector has received increased interest in terms of the adoption of Internet of things (IoT) services for learning activities. However, despite this interest, empirical studies on the adoption of IoT services in the smart classroom are limited. Thus, the present study examines students’ intention to use IoT services in the smart classroom. Towards this end, an integrated model based on the technology acceptance model (TAM), technology readiness index (TRI), and external factors (enjoyment, compatibility, and self-efcacy) is proposed. A quantitative research design was therefore used to determine the factors that afect students’ intention to use IoT services in the smart classroom, using a sample of 230 participants. The fndings showed that compatibility, discomfort, enjoyment, and self-efcacy had a signifcant infuence on both perceived ease of use (PEoU) and perceived usefulness (PU). Furthermore, innovativeness had a signifcant efect on PEoU, and insecurity had a signifcant impact on PU. The results also revealed that PU had a signifcant infuence on the students’ behavioural intention to use. These fndings extend the understanding of students’ intention to use IoT services in the smart classroom. This study could be benefcial to researchers, educators, and IoT developers. However, it also presents a number of limitations, such as a lack of qualitative methods and the small number of theories applied

Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach

This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages

Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach

This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages

Electrochemical defluorination of water: an experimental and morphological study

This experimental study concerns the elimination of fluoride from water using an electrocoagulation reactor having a variable flow direction in favour of increasing the electrolysing time, saving the reactor area, and water mixing. The detention time of the space-saver EC reactor (S-SECR) was measured and compared to the traditional reactors using an inert dye (red drain dye). Then, the influence of electrical current (1.5 ≤ δ ≤ 3.5 mA cm−2), pH of water (4 ≤ pH ≤ 10), and distance between electrodes (5 ≤ ϕ ≤ 15) on the defluoridation of water was analysed. The effect of the electrolysing activity on the electrodes' morphology was studied using scanning electron microscopy (SEM). Additionally, the operational cost was calculated. The results confirmed the removal of fluoride using S-SECR met the guideline of the World Health Organization (WHO) for fluoride levels in drinking water of ≤1.5 mg/L. S-SECR abated fluoride concentration from 20 mg/L to the WHO's guideline at δ, ϕ, pH, operational cost, and power consumption of 2.5 mA cm−2, 5 mm, 7, 0.346 USD m−3, and 5.03 kWh m−3, respectively. It was also found the S-SECR enhanced the detention time by 190% compared to the traditional reactors. The appearance of dents and irregularities on the surface of anodes in the SEM images proves the electrolysing process.Validerad;2022;Nivå 2;2022-04-29 (sofila)</p