Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial ( p = 1.43 ) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively ( p < 0.0001 ) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.

Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.

Nowadays, the demand for low-cost, compact, and interference rejected antennas with ultrawideband capability has been increased. Metamaterial-inspired loaded structures have capability of providing exceptional solutions for narrow range wireless communication and low consuming power while transmitting and receiving the signal. It is a difficult task to construct ideal metamaterial-inspired antennas with a variety of features such as extremely large bandwidth, notching out undesirable bands, and frequency. Metamaterial-inspired structures such as SRR and CSRR, and triangle-shaped TCSRR are most commonly used structures to achieve optimized characteristics in ultrawideband antennas. In this paper, an extensive literature survey is accomplished to get conception about metamaterial-inspired patch antennas. This review paper elucidates variants of metamaterial-inspired structures/resonators utilized in order to acquire sundry applications such as WiMAX, WLAN, satellite communication, and radar. Various researchers have used different methodology to design, stimulate, and analyze the metamaterial-inspired structure loaded antennas. Also, the results of different metamaterial-inspired antennas such as bandwidth, gain, return loss, and resonant frequency have been also represented in this paper. This manuscript also gives brief introduction about the metamaterial, its types, and then its application in microstrip patch antenna over the last decade. This manuscript throws light over the various studies conducted in the field of metamaterial-inspired antenna in the past. It has been seen that with the inclusion of metamaterial in conventional antenna, various characteristics such as impedance bandwidth, reflection coefficient, gain, and directivity have been improved. Also, frequency rejection of narrow bands which exits in ultrawideband frequency range can be done by embedding metamaterial-inspired structures such as SRR and CSRR.

Cancer is one of the top causes of death globally. Recently, microarray gene expression data has been used to aid in cancer’s effective and early detection. The use of DNA microarray technology to uncover information from the expression levels of thousands of genes has enormous promise. The DNA microarray technique can determine the levels of thousands of genes simultaneously in a single experiment. The analysis of gene expression is critical in many disciplines of biological study to obtain the necessary information. This study analyses all the research studies focused on optimizing gene selection for cancer detection using artificial intelligence. One of the most challenging issues is figuring out how to extract meaningful information from massive databases. Deep Learning architectures have performed efficiently in numerous sectors and are used to diagnose many other chronic diseases and to assist physicians in making medical decisions. In this study, we have evaluated the results of different optimizers on a RNA sequence dataset. The Deep learning algorithm proposed in the study classifies five different forms of cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) and Colon Adenocarcinoma (COAD). The performance of different optimizers like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), and Adaptive Momentum (AdaM). The experimental results gathered on the dataset affirm that AdaGrad and Adam. Also, the performance analysis has been done using different learning rates and decay rates. This study discusses current advancements in deep learning-based gene expression data analysis using optimized feature selection methods.

<abstract> <p>One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.</p> </abstract>