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Depression is the most prevalent mental health disorder, which is spreading like an epidemic. With the rise of the internet, people from all over the world now express their daily lives on social media, which can be utilized to analyze their mental health condition. Automated approaches like Machine Learning (ML) and Deep Learning (DL) are used to analyze social media posts. The ML techniques are performing well in detecting depression from monolingual text. Nevertheless, ML model’s low interpretability makes it difficult to identify depression in multilingual texts. Hence, the study focuses on seven languages for developing the model. Traditional ML and DL algorithms like Convolutional Neural Network (CNN), Support Vector Machine, Random Forest, and Decision Tree lack interpretability regarding prediction. To effectively analyze the multilingual dataset, CNN with a Hierarchical Attention Network is implemented in the model and then the Gaussian Naive Bayes algorithm is applied to the extracted output layer to generate an interpretable prediction, allowing it to offer enhanced explainability compared to other state-of-the-art algorithms. The proposed approach demonstrates effectiveness in analyzing multilingual texts with an accuracy of 89.09% outperforming other ML and DL models in terms of time and memory efficiency.

Brain hemorrhage refers to a potentially fatal medical disorder that affects millions of individuals. The percentage of patients who survive can be significantly raised with the prompt identification of brain hemorrhages, due to image-guided radiography, which has emerged as the predominant treatment modality in clinical practice. A Computed Tomography Image has frequently been employed for the purpose of identifying and diagnosing neurological disorders. The manual identification of anomalies in the brain region from the Computed Tomography Image demands the radiologist to devote a greater amount of time and dedication. In the most recent studies, a variety of techniques rooted in Deep learning and traditional Machine Learning have been introduced with the purpose of promptly and reliably detecting and classifying brain hemorrhage. This overview provides a comprehensive analysis of the surveys that have been conducted by utilizing Machine Learning and Deep Learning. This research focuses on the main stages of brain hemorrhage, which involve preprocessing, feature extraction, and classification, as well as their findings and limitations. Moreover, this in-depth analysis provides a description of the existing benchmark datasets that are utilized for the analysis of the detection process. A detailed comparison of performances is analyzed. Moreover, this paper addresses some aspects of the above-mentioned technique and provides insights into prospective possibilities for future research.

Internet of things (IoT) and software-defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. This study is aimed at reviewing the literature on SDN for IoT (SDN-IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. The study articulates the challenges of managing and orchestrating IoT systems and how SDN can be used to address these challenges by enabling dynamic and flexible network configurations. It delineates not only the function of blockchain (BC) technology in securing and managing IoT networks but also how SDN can be utilized to incorporate BC-based solutions. Additionally, the potential of SDN for mobile networks is explored, which are increasingly being used to support IoT devices. Finally, this study outlines the issues, challenges, and potential future research directions that may present opportunities for the researchers working in this field, underscoring the demand for more in-depth investigation and advancement.

COVID-19 is a serious worldwide health emergency that is affecting many nations. The financial standing and mental health of women are negatively impacted by such widespread epidemics. Thus, the purpose of this study was to evaluate, among married women in Bangladesh during the COVID-19 pandemic, the level of women’s economic empowerment and mental health and related determinants. The author used secondary research from numerous published research articles, review articles, and published international and national reports like the World Health Organization (WHO), UNDP, and United Nations (UN) to examine women’s economic empowerment and mental health during the COVID-19 pandemic. The COVID-19 condition made it more common for women to experience loneliness, melancholy, anxiety, and sleep disruption. Women face the greatest risk because they make up 70% of the healthcare workforce and work in caregiving facilities. According to COVID-19, the primary causes of poor mental health in Bangladesh were being unemployed, being obese, lack of concealing, and having no family. Bangladesh’s patriarchal family system and physical weakness of women leads to increased mental disorders, workplace dangers, abuse, exploitation, harassment, and physical harm during crises and quarantine. This will promote women’s economic empowerment and improve mental health conditions. For this reason, more research about these vulnerable populations is required.

Microorganisms offer a sustainable way to increase crop production and promote eco-friendly farming. The endophytic fungus Metarhizium anisopliae is known for its multiple roles in plant ecosystems, including plant protection, symbiosis, and abiotic stress mitigation. In this study, we evaluated the potential of seed priming with M. anisopliae isolate MetA1 (MA) to enhance germination, photosynthetic efficiency, growth, and yield of two wheat varieties, BARI Gom 26 (BG26) and BARI Gom 33 (BG33) under field conditions. The study demonstrated that MA seed priming significantly improved wheat germination (by 13% and 26.04%) of BG26 and BG33, respectively. Overall, photosynthetic performance, indicated by increased leaf angle, leaf thickness, relative chlorophyll content, and linear electron flow (LEF), quantum yield of Photo System II (Phi2) was increased in MA primed wheat plants, while reducing non-photochemical quenching like NPQt, PhiNO, PhiNPQ of both varieties. These enhancements were attributed to increased shoot biomass (by 215.64% for BG26 and 280.38% for BG33), root biomass (by 141.79% for BG26 and 207.4% for BG33), effective tiller percentage (by 9.17% for BG26 and 5.7% for BG33), spike length (by 25.05% for BG26 and 25.42% for BG33), grain yield parameters such as filled grain percentage (by 23.8% for BG26 and 12.5% for BG33), and grain weight per plant (by 168.62% for BG26 and 119.62% for BG33). The findings of the research demonstrated the potential of M. anisopliae for field use in an agricultural setting, providing a sustainable means of increasing food production.

Abstract In in-sensor image preprocessing, the sensed image undergoes low level processing like denoising at the sensor end, similar to the retina of human eye. Optoelectronic synapse devices are potential contenders for this purpose, and subsequent applications in artificial neural networks (ANNs). The optoelectronic synapses can offer image pre-processing functionalities at the pixel itself—termed as in-pixel computing. Denoising is an important problem in image preprocessing and several approaches have been used to denoise the input images. While most of those approaches require external circuitry, others are efficient only when the noisy pixels have significantly lower intensity compared to the actual pattern pixels. In this work, we present the innate ability of an optoelectronic synapse array to perform denoising at the pixel itself once it is trained to memorize an image. The synapses consist of phototransistors with bilayer MoS2 channel and p-Si/PtTe2 buried gate electrode. Our 7 × 7 array shows excellent robustness to noise due to the interplay between long-term potentiation and short-term potentiation. This bio-inspired strategy enables denoising of noise with higher intensity than the memorized pattern, without the use of any external circuitry. Specifically, due to the ability of these synapses to respond distinctively to wavelengths from 300 nm in ultraviolet to 2 µm in infrared, the pixel array also denoises mixed-color interferences. The “self-denoising” capability of such an artificial visual array has the capacity to eliminate the need for raw data transmission and thus, reduce subsequent image processing steps for supervised learning.