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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.

Abstract Oryza coarctata (2n = 4X = 48, KKLL) is an allotetraploid, undomesticated relative of rice and the only species in the genus Oryza with tolerance to high salinity and submergence. Therefore, it contains important stress and tolerance genes/factors for rice. The initial draft genome published was limited by data and technical restrictions, leading to an incomplete and highly fragmented assembly. This study reports a new, highly contiguous chromosome-level genome assembly and annotation of O. coarctata. PacBio high-quality HiFi reads generated 460 contigs with a total length of 573.4 Mb and an N50 of 23.1 Mb, which were assembled into scaffolds with Hi-C data, anchoring 96.99% of the assembly onto 24 chromosomes. The genome assembly comprises 45,571 genes, and repetitive content contributes 25.5% of the genome. This study provides the novel identification of the KK and LL genome types of the genus Oryza, leading to valuable insights into rice genome evolution. The chromosome-level genome assembly of O. coarctata is a valuable resource for rice research and molecular breeding.

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.

Machine learning (ML), sensors networks, and Internet of Things (IoT) are the most important contributor in the newest revolution in the industry. It is going towards a fully automated industrial environment where all the components including post production, pre production, supply chain and quality control would be automatically managed. Human will work more with brain, where all the physical efforts would be replaced by ML enabled intelligent robots that will perform all the tasks, where sensor networks will collect live information from the environment. All the decision would be taken on the fly from the previous records. In this work, we have tried to shed some light on the current involvement of ML and IoT in industry 4.0 environment. 28 articles were reviewed in this work which were selected through a selection process and were published between 2017 and 2022. Different tools, protocols, algorithms and the latest technologies used in the industry 4.0 environment have been analytically discussed in this work. Research gaps were tried to found out and some recommendations were provided which can be a pathway to research advancement related to industry 4.0.

Polycystic Ovary Syndrome (PCOS) is a critical hormonal disorder of women that significantly impacts life. In this new generation, women are more prone to PCOS. It is the cause of various problems, including infertility. Early detection of PCOS can reduce complexity. Therefore, an early and proper PCOS detection system is essential to minimize complications. Among all the detection techniques Machine Learning (ML) has an excellent performance in detection for its feature extraction capability. Therefore, considerable research has been carried out to detect PCOS using ML. Various ML approaches like Convolutional Neural Network, Support Vector Machine, K-Nearest-Neighbors, Random Forest, Logistic Regression, Decision Tree, Naive Bayes, etc., are used in detecting PCOS. This research aims to call attention to the researchers by presenting a descriptive and contextual overview of all the existing technologies on PCOS detection by ML algorithms. A comprehensive analysis is carried out of how various ML approaches have been used in PCOS detection over the last few decades, and the techniques are discussed thoroughly. A complete examination was studied on different datasets used in PCOS detection. The performance of several algorithms is compared in quantitative and qualitative approaches. Finally, the significant difficulties and future research scopes are discussed to draw a conclusion.

Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

Abstract Salinity has a significant negative impact on production of rice. To cope with the increased soil salinity due to climate change, we need to develop salt tolerant rice varieties that can maintain their high yield. Rice landraces indigenous to coastal Bangladesh can be a great resource to study the genetic basis of salt adaptation. In this study, we implemented a QTL analysis framework with a reciprocal mapping population developed from a salt tolerant landrace Horkuch and a high yielding rice variety IR29. Our aim was to detect genetic loci that contributes to the salt adaptive responses of the two different developmental stages of rice which are very sensitive to salinity stress. We identified 14 QTLs for 9 traits and found that most are unique to specific developmental stages. In addition, we detected a significant effect of the cytoplasmic genome on the QTL model for some traits such as leaf total potassium and filled grain weight. This underscores the importance of considering cytoplasm-nuclear interaction for breeding programs. Finally, we identified QTLs co-localization for multiple traits that highlights the possible constraint of multiple QTL selection for breeding programs due to different contributions of a donor allele for different traits.

This research deals with the petrography and mineral chemistry of gabbroic and monzodioritic rocks to characterize the minerals, including their genesis and tectonic evolution. The rocks are dominated by gabbro in Barapaharpur and monzodiorite in Voktipur, and they contain plagioclase, hornblende, biotite, and magnetite, with few epidote and apatite, as well as clinopyroxene and orthopyroxene in gabbroic member. Chemical analyses of minerals were carried out by an electron microprobe analyzer. The chemistry of plagioclases is compositionally oligoclase-andesine and andesine, amphiboles are dominantly magnesiohornblende, biotites are Mg-biotites (phlogopite), and pyroxenes are diopside and enstatite types. The calculated P-T conditions of the gabbroic rocks yielded about 850–957 °C and 5.8–9.3 kbar, which are the highest geothermobarometric values in the region. However, the monzodioritic rocks yielded about 696–723 °C and 5.6–6.2 kbar. The estimated emplacement depths from the calculated highest pressure values equalize at ∼ 20–33 km and at ∼ 19–22 km, respectively. Biotite chemistry provides quantitative fO2 within QFM and HM buffers ranging from −10.6 to −12.0 in gabbroic rocks and −11.6 to −13.5 in quartz monzodiorite, respectively. These data are also echoed by amphibole and pyroxene mineral chemistry, which collectively demonstrate that the source materials were in a relatively higher oxidation state within arc magmatism. The mineral data also offer calc-alkaline I-type suites formed within subduction-related environments. However, gabbroic rocks show a dominantly island arc signature substantiating the suprasubduction zone, and also have the affinity of the island arc tholeiitic and calc-alkaline basaltic equivalents within orogenic environments and marginally boninitic affinities.

This study focused on the role of safari parks as a sustainable source of income and employment to ensure food security of the corresponding employees. The survey was accomplished from two safari parks among the employees through a pretested questionnaire using simple random sampling techniques. Descriptive and inferential statistics was applied to evaluate current situation of different socioeconomic characteristics of the respondents. Different security dimension say, Economic Security Index (ESI), Health Security Index (HSI), Food Security Index (FSI) and Educational Security Index (EDUSI) was constructed to create the ultimate overall Livelihood Security Index (LSI). Again, simultaneous equation system was used to see the interrelation and amount of influence of different variables that affect livelihood status of the people. Three Stage Least Square (3SLS) estimation procedure was used to estimate the parameters of the equations. The study revealed that overall livelihood of the household was positively changed where food security was significantly influenced by the mode of employment. In order to sustainable income generation and employment opportunities, it is needed to boost the capacity of the geoparks and awareness building initiatives. Government can take initiative for promoting geotourism through infrastructural development so that different stakeholders will take initiatives to create a number of local jobs such as shop keeping, hotel and restaurants, transportations, guides, etc. for sustainable economic development through Safari parks.

The outbreak of Coronavirus Disease 2019 (Covid-19) had an enormous impact on humanity. Till May 2021, almost 172 million people have been affected globally due to the contagious spread of Covid-19. Although the distribution of vaccines has been started, the worldwide mass distribution is yet to happen. According to the World Health Organization (WHO), wearing a facemask can reduce the contagious spread of Covid-19 significantly. The governments of different countries have recommended implementing the “no mask, no service” method to impede the spread of Covid-19. However, even the improper wearing of a facemask can obstruct the goal and lead to the spread of the virus. Therefore, to ensure public safety, a system for monitoring facemasks on faces, commonly known as a facemask detection algorithm, is essential for overcoming this crisis. The facemask detection algorithms are part of the object detection algorithms which are used to detect objects in an image. Among the various object detection algorithms, deep learning showed tremendous performance in facemask detection for its excellent feature extraction capability than the traditional machine learning algorithms. However, there remains a lot of scope for future research to build an efficient facemask detection system. Therefore, this study aims to draw attention to the researchers by providing a narrative and meta-analytic review on all the published works related to facemask detection in the context of Covid-19. Because facemask detection algorithms are run mainly by adopting object detection algorithms, this paper also explores the progress of object detection algorithms over the last few decades. A comprehensive analysis of different datasets used in facemask detection techniques by many studies has been explored. The performance comparison among these algorithms is discussed in narrative and meta-analytic approaches. Finally, this study concludes with a discussion of some of the major challenges and future scope in the related field.

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this article provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

The world is facing an urgent need to provide secure communication and data access control in advanced metering infrastructure (AMI) because conventional cryptographic key management and authentication protocols are at stake. The cryptography schemes entirely rely on trusted third parties (TTPs), leading to a single point of failure and increasing network overhead. In response to this inefficiency and security compromise, this study proposes a blockchain-enabled distributed AMI secure communication scheme. In the proposed work, smart contract (SC), an integrated part of the blockchain, is programmed to substitute traditional TTP-based transaction systems, which operate in a distributed, immutable, and trustworthy manner. In this paper, we implemented practical Byzantine fault tolerance (PBFT) consensus algorithm and Hyperledger Fabric (HLF) blockchain platform to ensure Byzantine fault tolerance in the blockchain transaction. Performance analysis shows that the proposed BC-AMI scheme has the advantage of incurring the least amount of communication and time costs compared with similar studies while ensuring security against some common cyber-attacks.

Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.

Bangladesh lies within the origin of rice biodiversity and is home to many genotypes with varying amylose content and grain size. There is however no precise data on their amylose content and correlation with grain size and type. Yet these classifications governs the taste, cooking and nutritional quality of rice, ultimately determining its consumer preference and market price. Correlating grain size with amylose content will be useful information for Diabetics, particularly due to the unproven assumption that long grains correlate with high amylose content and vice versa. In this study, 131 long, medium and short grain Bangladeshi rice cultivars were analyzed and classified into high, intermediate, low or very low (H, I, L or VL) amylose slender or bold grain types. Both short and medium grain cultivars have mostly H or I amylose. The maximum amount of L and VL amylose was found among the long grain cultivars which also contain H and I amylose-containing rice. Therefore we could clearly show that long grains are associated with L and VL amylose, rather than medium and short grain. The microsatellite marker (RM190) and CAPS marker (RM190F-GBSSW2R/AccI) are linked to the gene responsible for amylose content and can be used for distinguishing variants. Thirty-five cultivars with all grain sizes were found to have four RM190 alleles (1–4: 100, 110, 116 and 120bp). Cultivars having VL amylose were identified by RM190F-GBSSW2R/AccI cleavage pattern. High amylose-containing rice was found mostly associated with class 1 and 2 alleles. The intermediate amylose group was characterized by the absence of the class 1 allele. Therefore we could distinguish VL, H and I amylose rice with simple tests with the rest belonging to the L group. The study thus demonstrated the use of simple tests in the categorization of rice, based on amylose groups. Keywords: Amylose content, CAPS, Single nucleotide polymorphism (SNP), Restriction enzyme (AccI)

The use of deoxyribonucleic acid (DNA) hybridization to detect disease-related gene expression is a valuable diagnostic tool. An ion-sensitive field-effect transistor (ISFET) with a graphene layer has been utilized for detecting DNA hybridization. Silicene is a two-dimensional silicon allotrope with structural properties similar to graphene. Thus, it has recently experienced intensive scientific research interest due to its unique electrical, mechanical, and sensing characteristics. In this paper, we proposed an ISFET structure with silicene and electrolyte layers for the label-free detection of DNA hybridization. When DNA hybridization occurs, it changes the ion concentration in the surface layer of the silicene and the pH level of the electrolyte solution. The process also changes the quantum capacitance of the silicene layer and the electrical properties of the ISFET device. The quantum capacitance and the corresponding resonant frequency readout of the silicene and graphene are compared. The performance evaluation found that the changes in quantum capacitance, resonant frequency, and tuning ratio indicate that the sensitivity of silicene is much more effective than graphene.

The study was conducted to find out the Present scenario of green banking practices in Bangladesh. Green banking is not just a corporate social responsibility activity; it is all about going beyond to keep the world livable without any significant damage. Green banking that considers all the social and environmental factors is also called ethical banking. The main objective of green Banking is to ensure the use of organizational resources in favor of the environment and society. Green banking as a concept is proactive and smart way of thinking with a vision for future sustainability of our only Spaceship earth. Bangladesh Bank is the first central bank in the world that has taken real initiatives to facilitate the way of Green banking practice. With some specific guidelines and legal frameworks the central bank of Bangladesh has been urging all the commercial banks in Bangladesh to develop their own Green Banking Policies.

Bangladesh is a reservoir of diverse rice germplasm and is home to many landraces with unique, important traits. Molecular characterization of these landraces is of value for their identification, preservation, and potential use in breeding programs. Thirty-eight rice landraces from different regions of Bangladesh including some high yielding BRRI varieties were analyzed by 34 polymorphic microsatellite markers yielding a total of 258 reproducible alleles. The analysis could locate 34 unique identifiers for 21 genotypes, making the latter potentially amenable to identity verification. An identity map for these genotypes was constructed with all the 12 chromosomes of the rice genome. Polymorphism information content (PIC) scores of the 34 SSR markers were 0.098 to 0.89 where on average 7.5 alleles were observed. A dendogram constructed using UPGMA clustered the varieties into two major groups and five subgroups. In some cases, the clustering matched with properties like aromaticity, stickiness, salt tolerance, and photoperiod insensitivity. The results will help breeders to work towards the proper utilization of these landraces for parental selection and linkage map construction for discovery of useful alleles.

The objective of the experiment was to investigate the effects of dietary supplementation with 2-hydroxy-4-(methylthio)-butanoic acid isopropyl ester (HMBi) on the nitrogen (N) metabolism in beef steers. The plasma metabolites analyzed by metabolome profiling were used to clarify the impact mechanism. Three Simmental steers (body weight, 593 ± 23 kg) were used as experimental animals. Three levels of HMBi (i.e., 0, 12, and 24 g d−1) were added in a basal ration as experimental treatments. The steers and the dietary treatments were randomly allocated in a 3 × 3 Latin square design. The results showed that supplementing HMBi up to 24 g d−1 did not affect the N retention and N retention rate (NRR), and the fecal N/urinary N ratio even though it tended to linearly increase the uric acid N/urinary N ratio in steers. The results of plasma metabolome profiling showed that supplementing HMBi at 24 g d−1 upregulated the plasma concentrations of L-methionine (Met); Met-related metabolites including betaine, Met sulfoxide, and taurine; and L-isoleucine and tyrosine, whereas it downregulated L-serine, glycine, diaminopimelic acid, and other metabolites. The reason for the nonsignificant effect of HMBi on improving the N utilization in steers could be that the steers used in the experiment were in the fattening period. It is suggested to evaluate the effects of the dietary addition of HMBi using growing cattle in further research.