Towards Enhanced Identification of Emotion from Resource-Constrained Language through a novel Multilingual BERT Approach

Emotion identification from text has recently gained attention due to its versatile ability to analyze human-machine interaction. This work focuses on detecting emotions from textual data. Languages, like English, Chinese, and German are widely used for text classification, however, limited research is done on resource-poor oriental languages. Roman Urdu (RU) is a resource-constrained language extensively used across Asia. This work focuses on predicting emotions from RU text. For this, a dataset is collected from different social media domains and based on Paul Ekman\u27s theory it is annotated with six basic emotions, i.e., happy, surprise, angry, sad, fear, and disgusting. Dense word embedding representations of different languages is adopted that utilize existing pre-trained models. BERT is additionally pre-trained and fine-tuned for the classification task. The proposed approach is compared with baseline machine learning and deep learning algorithms. Additionally, a comparison of the current work is also performed with different approaches for the same task. Based on the empirical evaluation, the proposed approach performs better than the existing state-of-the-art with an average accuracy of 91%

A secure food supply chain solution: blockchain and IoT-enabled container to enhance the efficiency of shipment for strawberry supply chain

The supply chain systems in the food industry are complex, including manufacturers, dealers, and customers located in different areas. Currently, there is a lack of transparency in the distribution and transaction processes of online food trade. The global food supply chain industry has enormous hurdles because of this problem, as well as a lack of trust among individuals in the sector and a reluctance to share information. This study aims to develop a blockchain-based strawberry supply chain (SSC) framework to create a transparent and secure system for tracking the movement of strawberries from the farm to the consumer. Using Ethereum smart contracts, the proposed solution monitors participant interactions, triggers events, and logs transactions to promote transparency and informed decision-making. The smart contracts also govern interactions between vendors and consumers, such as monitoring the status of Internet of Things (IoT) containers for food supply chains and notifying consumers. The proposed framework can be extended to other supply chain industries in the future to increase transparency and immutability

A Review on Strong Impacts of Thermal Stress on Plants Physiology, Agricultural Yield; and Timely Adaptation in Plants to Heat Stress

In this review, we checked the harsh influence of high temperature or heat stress on plant metabolism and crop yield. Plants can bear a minimum range of temperature; temperature more than this optimum range comes in the term of heat stress. Climate changes increase the number and severity of heat waves that reduced the development of plants and resulted in the death of the entire plant. Heat stress is a major stressful environment that destroys plant growth, biochemical reactions, and the yield of crops across the world. High-temperature influences many physiological and chemical reactions in plants. HS is now a big deal for crop production and the essential goal of agriculture is to maintain a high yield of crops. A plant lives in the conditions of high temperature based on its capacity to receive the HT stimulus, generate and change the signal, and then initiate physiological and biochemical changes. The plants show physiological and biochemical responses to heat the stress, is an active area of research. To deal with HT, different molecular techniques are in progress. After thoroughly reviewed of the different discoveries on plants’ responses, adaptation, and forbearance to HT at the cellular, organelles, and entire plant levels, this article described several approaches that could be taken to increase thermo- forbearance in plants

Phytochemical analysis and biological activity of some local and imported walnut

To study the bioactive substances of the walnut kernel of six cultivars which were newly selected from Kurdistan-Iraq and one type import from America, analyzed for their phytochemical contents and antioxidant activities. HPLC (High-performance liquid chromatography) was used for phenolic compound estimation, GC (Gas chromatography) for fatty acid analysis, and DPPH (2,2-diphenyl-1-picrylhydrazyl) radical-scavenging. In terms of fatty acids, palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid of all seven types we analyzed, all of them had a significant result. As for antioxidants, the same as before, the antioxidants were significant for all the chosen samples. In terms of phenolic compounds, quinic acid, gallic acid, 1,2,3,6 trigalloyl glucose, vanillic acid, syringic acid, and rutin, all types were significant as well. Finally, our results show that most of them were of high significance. Some regions in the Kurdistan region of Iraq showed high results for important secondary products, while the American counterpart is lower but still better than some of the Kurdistan region walnut

WiFOG: Integrating deep learning and hybrid feature selection for accurate freezing of gait detection

This study investigates the feasibility of utilizing non-invasive WiFi sensing using the 4.8 GHz operating frequency band of the 5 G spectrum, which is suitable for Internet of Things applications. We propose WiFOG: a WiFi CSI system for detecting FOG in PD leveraging deep learning and wireless channel characteristics collected by wireless devices such as a radio frequency signal generator, a network interface card, and dipole antennas. The raw data for several activities, including sitting, slow-walking, fast-walking, voluntary stopping, and FOG episodes, is collected. Regress feature engineering is performed in which discrete wavelet transforms is used for signal denoising and Hilbert-Huang transforms for feature extraction. Further, we propose hybrid feature selection techniques based on whale optimization, recursive feature elimination, and select form models for dimensionality reduction. Moreover, we propose a deep-gated recurrent network (DGRU) for activity classification and FOG detection and compared the results with the state-of-the-art approaches in the existing work. The results show our proposed scheme surpasses existing FOG detection with a total improvement of approximately 4% in accuracy and a 29% reduction in training time

Diverse drivers of unsustainable groundwater extraction behaviour operate in an unregulated water scarce region

Depletion of groundwater resources is of increasing concern in many parts of the world; however, farmers'perceptions of resource status and the role these have in influencing decisions about groundwater use are rarely considered and even more rarely analysed. This paper investigates the links between farmers' perceptions of resource condition and drivers of groundwater decline and patterns of groundwater use in the semi-arid highland region of Balochistan, Pakistan. Key factors associated with groundwater over-exploitation in this region, identified by farmers, include: high returns from irrigated fruit and vegetable cultivation; drought; mass installation of tubewells; inefficient irrigation practices; government policies and subsidies that promote groundwater development; and lack of effective groundwater governance. Critically, while a majority of farmers in this study believe that groundwater is a limited resource, there is little evidence to indicate that this then leads to sustainable groundwater use decision making within these communities. Without effective intervention, groundwater resources in this region will potentially suffer the consequences of human behaviour associated with the use of common pool resources identified in Hardin's (1968) seminal ‘Tragedy of the Commons’ paper. This study exemplifies the importance to the future of rural communities in water scarce regions of effective governance, regulations and economic incentives for sustainable water management

Metaverse Security: Issues, Challenges and a Viable ZTA Model

The metaverse is touted as an exciting new technology amalgamation facilitating next-level immersive experiences for users. However, initial experiences indicate that a host of privacy, security and control issues will need to be effectively resolved for its vision to be realized. This paper highlights the security issues that will need to be resolved in the metaverse and the underlying enabling technologies/platforms. It also discussed the broader challenges confronting the developers, the service providers and other stakeholders in the metaverse ecosystem which if left unaddressed may hamper its broad adoption and appeal. Finally, some ideas on building a viable Zero-Trust Architecture (ZTA) model for the metaverse are presented

Coexistence of Lupus Nephritis, Ulcerative Colitis, and Communicating Hydrocephalus: A Report of a 21-Year-Old Male

Systemic lupus erythematosus (SLE) and ulcerative colitis (UC) are multisystem autoimmune disorders that rarely coexist. We report a case history of a 21-year-old male, presenting with bloody diarrhea and, later, diagnosed to have ulcerative colitis on colonic biopsy. There was clinically silent renal impairment leading to end-stage kidney disease requiring hemodialysis possibly secondary to ongoing lupus nephritis as suggested by positive lupus-specific antibodies’ detection. Besides this, the diagnosis of lupus associated with early communicating hydrocephalus was made on CT brain findings which clinically responded well to the initiation of immunosuppressive therapy. It is imperative to keep in mind the remote possibility of ulcerative colitis in an SLE patient with gastrointestinal (GI) manifestations. Communicating hydrocephalus is a rare neurological manifestation of SLE leading to seizures and can respond well to the initiation of steroids and immunosuppressants. Therefore, a trial of immunosuppressant medications must be given even in a patient with end-stage renal disease (ESRD) to halter extra renal rare lupus manifestations

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

Abstract A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder–Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance