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Brain tumors are one of the most fatal disorders that cause one of the highest mortalities in the world. Gliomas are the most common primary brain tumors originating from glial cells in the central nervous system. Traditionally, a tissue sample is extracted and examined for its genetic and characteristic properties. This method is invasive, painful, and takes a longer period to produce results. Various automatic Deep learning (DL) based schemes have been presented for the brain glioma detection, but they lack due to poor explainability, lower generalization, poor feature depiction, class imbalance problem and lower detection rate. This paper presents a deep learning based brain tumor detection using two way feature depiction model (TWFDM) that combines the 2D-Deep Convolution Neural Network (DCNN) and 1D-DCNN. The 2D-DCNN accepts the raw MRI images and the 1D-DCNN accepts the handcrafted local binary pattern (LBP), gray level cooccurrence matrix (GLCM), and Histogram of Oriented Gradient (HOG) features. Furthermore, improved particle swarm optimization (IPSO) is used for feature selection to minimize the computational complexity of the TWFDM system. The proposed TWFDM achieves an overall accuracy of 96.25%, a recall of 96.34%, a precision of 96.31%, and an F1-score of 96.32% on the Brain MRI dataset for four-class classification, representing an important improvement over traditional techniques.

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) systems for the purpose of creating a comparative evaluation of the performance of the six distinct algorithms. The goal of this analysis is to determine which of the six algorithms has the best overall performance. In the study, the performance of managing the training dataset is compared across the algorithms. The maximum power point tracking energy harvesting system is created using the environment of MATLAB or Simulink, and the produced model is examined using the artificial neural network toolkit. A total of 1000 datasets of solar irradiance, temperature, and voltage were used to train the suggested model. The data are split into three categories: training, validation, and testing. Eighty percent of the total data is used for training the model, and the remaining twenty percent is divided equally for testing and validation. According to the results, the regression values of LM, RP, BR, and BFGS are 1, whereas the regression values for SCG and GDM are less than 1. The gradient values for LM, RP, BFGS, SCG, BR, and GDM are 7.983 × 10−6, 0.033415, 1.0211 × 10−7, 0.14161, 0.00010493, and 11.485, respectively. Similarly, the performance values for these algorithms are 2.0816 × 10−10, 2.8668 × 10−6, 9.98 × 10−17, 0.052985, 1.583 × 10−7, and 0.15378. Overall, the results demonstrate that the LM and BFGS algorithms exhibit superior performance in terms of gradient and overall performance. The RP and BR algorithms also perform well across various metrics, while the SCG and GDM algorithms show comparatively less effectiveness in addressing the proposed problem. These findings provide valuable insights into the relative performance of the six evaluated algorithms for MPPT energy harvesting in solar PV systems.

This paper presents a compact 1 × 4 antipodal Vivaldi antenna (AVA) array for 5G millimeter-wave applications. The designed antenna operates over 24.19 GHz–29.15 GHz and 30.28 GHz–40.47 GHz frequency ranges. The proposed antenna provides a high gain of 8 dBi to 13.2 dBi and the highest gain is obtained at 40.3 GHz. The proposed antenna operates on frequency range-2 (FR2) and covers n257, n258, n260, and n261 frequency bands of 5G communication. The corrugations and RT/Duroid 5880 substrate are used to reduce the antenna size to 24 mm × 28.8 mm × 0.254 mm, which makes the antenna highly compact. Furthermore, the corrugations play an important role in the front-to-back ratio improvement, which further enhances the gain of the antenna. The corporate feeding is optimized meticulously to obtain an enhanced bandwidth and narrow beamwidth. The radiation pattern does not vary over the desired operating frequency range. In addition, the experimental results of the fabricated antenna coincide with the simulated results. The presented antenna design shows a substantial improvement in size, gain, and bandwidth when compared to what has been reported for an AVA with nearly the same size, which makes the proposed antenna one of the best candidates for application in devices that operate in the millimeter frequency range.

This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper statistical evaluation is done on the predictions using such performance measures as (1) area under the receiver operating characteristic curve, (2) Cohen’s kappa parameter, and (3) confusion matrix. Experimental results show that the proposed VGG-19 architecture coupled with transfer learning is an effective technique for automatically detecting the disease state of a retina.

This research outlines the design of capacitive pressure sensors fabricated from three biocompatible materials, featuring both circular and square geometries. The sensors were structured with a dielectric layer positioned between gold-plated electrodes at the top and bottom. Their performance was assessed through simulations conducted with ANSYS Workbench. Of the various sensor configurations tested, the circular design that included two crescent-shaped slots and a 20 µm thick PDMS dielectric material demonstrated the highest sensitivity of 10.68 fF/mmHg. This study further investigated the relationship between resonant frequency shifts and arterial blood pressure, revealing an exceptionally linear response, as evidenced by a Pearson’s correlation coefficient of −0.99986 and an R-squared value of 0.99972. This confirmed the sensor’s applicability for obtaining precise blood pressure measurements. Additionally, a 3 × 30 mm cobalt–chromium (Co-Cr) stent was obtained, and its inductance was measured using an impedance analyzer.

A compact, low-profile, coplanar waveguide (CPW)-fed quad-port multiple-input–multiple-output (MIMO)/diversity antenna with triple band-notched (Wi-MAX, WLAN, and X-band) characteristics is proposed for super-wideband (SWB) applications. The proposed design contains four similar truncated–semi-elliptical–self-complementary (TSESC) radiating patches, which are excited through tapered CPW feed lines. A complementary slot matching the radiating patch is introduced in the ground plane of the truncated semi-elliptical antenna element to obtain SWB. The designed MIMO/diversity antenna displays a bandwidth ratio of 31:1 and impedance bandwidth (|S11| ≤ − 10 dB) of 1.3–40 GHz. In addition, a complementary split-ring resonator (CSRR) is implanted in the resonating patch to eliminate WLAN (5.5 GHz) and X-band (8.5 GHz) signals from SWB. Further, an L-shaped slit is used to remove Wi-MAX (3.5 GHz) band interferences. The MIMO antenna prototype is fabricated, and a good agreement is achieved between the simulated and experimental outcomes.

The application of Internet of Things (IoT) has been emerging as a new platform in wireless technologies primarily in the field of designing electric vehicles. To overcome all issues in existing vehicles and for protecting the environment, electric vehicles should be introduced by integrating an intellectual device called sensor all over the body of electric vehicle with less cost. Therefore, this article confers the need and importance of introducing electric vehicles with IoT based technology which monitors the battery life of electric vehicles. Since the electric vehicles are implemented with internet, an online monitoring system which is called Things Speak has been used for monitoring all the vehicles in a continuous manner (day-by-day). These online results will then be visualized in MATLAB after an effective boosting algorithm is integrated with objective function. The efficiency of proposed method is tested by visual analysis and performance results prove that the projected method on electric vehicle is improved when using IoT based technology. It is also observed that cost of implementation is lesser and capacity of electric vehicle is increased to about 74.3% after continuous monitoring with sensors.

This article presents a compact, planar, quad-port ultra-wideband (UWB) multiple-input–multiple-output (MIMO) antenna with wide axial ratio bandwidth (ARBW). The proposed MIMO design consists of four identical square-shaped antenna elements, where each element is made up of a circular slotted ground plane and feed by a 50 Ω microstrip line. The circular polarization is achieved using a protruding hexagonal stub from the ground plane. The four elements of the MIMO antenna are placed orthogonally to each other to obtain high inter-element isolation. FR-4 dielectric substrate of size 45 × 45 × 1.6 mm3 is used for the antenna prototype, and a good agreement is noticed among the simulated and experimental results. The proposed MIMO antenna shows 3-dB ARBW of 52% (3.8–6.5 GHz) and impedance bandwidth (S11 ≤ −10 dB) of 144% (2.2–13.5 GHz).

The Advanced Encryption Standard (AES-128) is widely used in high-speed secure communication systems, requiring efficient ASIC-oriented hardware implementations. To ensure that the implementation achieves full functional correctness and coverage, this work provides an AES-128 hardware implementation in Systemverilog, which has been tested using an environment based on the Universal Verification Methodology (UVM). Post-synthesis evaluation provides realistic timing, area, and power metrics using Cadence Genus. Pipelining techniques were employed to reduce the critical path delay, along with enabling maximum operating frequency. Competitive area utilisation and power efficiency were also maintained. The proposed architecture demonstrates improved timing efficiency compared with representative FPGA-based implementations. This paper presents a fully pipelined ASIC-oriented AES-128, 300 ps combinational critical path delay and 1.39 GHz operating frequency.

Background: Anthralin is known for its efficacy in treating psoriasis and acne, possessing poor solubility. Addressing these limitations, the present study endeavors to develop a microemulgel formulation of anthralin aimed at enhancing solubility. Method: The solubility study was performed in various solvents. An o/w (oil-in-water) emulsion was formed using the water titration method, which was optimized by statistical experimental design half-run CCD. The final optimized batch was evaluated for physicochemical and in vitro properties Result: The final optimized batch showed a particle size (PS) of 417 nm, −25.2 mV zeta potential (ZP) and pH 5.8, which remained stable upon centrifugation, heating–cooling and freeze–thawing cycle. Furthermore, microemulsion with Carbopol 943 5% w/v was selected as the gel base for the formation of microemulgel characterized by PS, ZP, pH, and viscosity of 230 nm, −50.6 mV, 6.9 and 14,200 cps, respectively, that ensured it a high enough stability. In silico molecular docking between ligand and protein provides the binding energies validating the interaction. Hence, the in silico study was performed for psoriasis and P. acne proteins. An in vitro antibacterial activity study on Propionibacterium revealed a significant efficiency of the formulation and MTT assay using L929 cell line in the presence of the drug-loaded microemulgel indicated an inhibition of growth proving that formulation has anti-psoriatic activity. Conclusions: Combination therapy with Clindamycin might improve efficacy while reducing antibiotic resistance risks.