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%

System-wide analyses of the fission yeast poly(A)+ RNA interactome reveal insights into organization and function of RNA–protein complexes

Large RNA-binding complexes play a central role in gene expression and orchestrate production, function, and turnover of mRNAs. The accuracy and dynamics of RNA–protein interactions within these molecular machines are essential for their function and are mediated by RNA-binding proteins (RBPs). Here, we show that fission yeast whole-cell poly(A)+ RNA–protein crosslinking data provide information on the organization of RNA–protein complexes. To evaluate the relative enrichment of cellular RBPs on poly(A)+ RNA, we combine poly(A)+ RNA interactome capture with a whole-cell extract normalization procedure. This approach yields estimates of in vivo RNA-binding activities that identify subunits within multiprotein complexes that directly contact RNA. As validation, we trace RNA interactions of different functional modules of the 3′ end processing machinery and reveal additional contacts. Extending our analysis to different mutants of the RNA exosome complex, we explore how substrate channeling through the complex is affected by mutation. Our data highlight the central role of the RNA helicase Mtl1 in regulation of the complex and provide insights into how different components contribute to engagement of the complex with substrate RNA. In addition, we characterize RNA-binding activities of novel RBPs that have been recurrently detected in the RNA interactomes of multiple species. We find that many of these, including cyclophilins and thioredoxins, are substoichiometric RNA interactors in vivo. Because RBPomes show very good overall agreement between species, we propose that the RNA-binding characteristics we observe in fission yeast are likely to apply to related proteins in higher eukaryotes as well

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

SA Sorting: A Novel Sorting Technique for Large-Scale Data

Sorting is one of the operations on data structures used in a special situation. Sorting is defined as an arrangement of data or records in a particular logical order. A number of algorithms are developed for sorting the data. The reason behind developing these algorithms is to optimize the efficiency and complexity. The work on creating new sorting approaches is still going on. With the rise in the generation of big data, the concept of big number comes into existence. To sort thousands of records either sorted or unsorted, traditional sorting approaches can be used. In those cases, we can ignore the complexities as very minute difference exists in their execution time. But in case the data are very large, where execution time or processed time of billion or trillion of records is very large, we cannot ignore the complexity at this situation; therefore, an optimized sorting approach is required. Thus, SA sorting is one of the approaches developed to check sorted big numbers as it works better on sorted numbers than quick sort and many others. It can also be used to sort unsorted records as well

CPIDM: A Clustering-Based Profound Iterating Deep Learning Model for HSI Segmentation

The existing work on unsupervised segmentation frequently does not present any statistical extent to estimating and equating procedures, gratifying a qualitative calculation. Furthermore, regardless of the datum that enormous research is dedicated to the advancement of a novel segmentation approach and upgrading the deep learning techniques, there is an absence of research comprehending the assessment of eminent conventional segmentation methodologies for HSI. In this paper, to moderately fill this gap, we propose a direct method that diminishes the issues to some extent with the deep learning methods in the arena of a HSI space and evaluate the proposed segmentation techniques based on the method of the clustering-based profound iterating deep learning model for HSI segmentation termed as CPIDM. The proposed model is an unsupervised HSI clustering technique centered on the density of pixels in the spectral interplanetary space and the distance concerning the pixels. Furthermore, CPIDM is a fully convolutional neural network. In general, fully convolutional nets remain spatially invariant preventing them from modeling position-reliant outlines. The proposed network maneuvers this by encompassing an innovative position inclined convolutional stratum. The anticipated unique edifice of deep unsupervised segmentation deciphers the delinquency of oversegmentation and nonlinearity of data due to noise and outliers. The spectrum efficacy is erudite and incidental from united feedback via deep hierarchy with pooling and convolutional strata; as a consequence, it formulates an affiliation among class dissemination and spectra along with three-dimensional features. Moreover, the anticipated deep learning model has revealed that it is conceivable to expressively accelerate the segmentation process without substantive quality loss due to the existence of noise and outliers. The proposed CPIDM approach outperforms many state-of-the-art segmentation approaches that include watershed transform and neuro-fuzzy approach as validated by the experimental consequences

Machine learning based model for detecting depression during Covid-19 crisis

Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression.Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them

Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework

Modern big data applications tend to prefer a cluster computing approach as they are linked to the distributed computing framework that serves users jobs as per demand. It performs rapid processing of tasks by subdividing them into tasks that execute in parallel. Because of the complex environment, hardware and software issues, tasks might run slowly leading to delayed job completion, and such phenomena are also known as stragglers. The performance improvement of distributed computing framework is a bottleneck by straggling nodes due to various factors like shared resources, heavy system load, or hardware issues leading to the prolonged job execution time. Many state-of-the-art approaches use independent models per node and workload. With increased nodes and workloads, the number of models would increase, and even with large numbers of nodes. Not every node would be able to capture the stragglers as there might not be sufficient training data available of straggler patterns, yielding suboptimal straggler prediction. To alleviate such problems, we propose a novel collaborative learning-based approach for straggler prediction, the alternate direction method of multipliers (ADMM), which is resource-efficient and learns how to efficiently deal with mitigating stragglers without moving data to a centralized location. The proposed framework shares information among the various models, allowing us to use larger training data and bring training time down by avoiding data transfer. We rigorously evaluate the proposed method on various datasets with high accuracy results

Categorizing threat types and cyber-assaults over Internet of Things-equipped gadgets

Internet of Things (IoT) gadgets are very attractive globally, and IoT facilities are universal. Their progress has not been ignored, and the number of invaders and outbreaks on IoT and facilities is growing. Because cyber-assaults are zero-trust to the IoT, and the IoT is deeply ingrained in our lives and culture, we must embrace a comprehensive Zero-Trust strategy for cyber defense. As a result, the new model is no longer vulnerable to fear and outbreak on the IoT structure. In this article, an effort is made to categorize threat types and analyze and explain invaders and assaults on IoT gadgets and facilities

Compact Ultrawide Band Metamaterial-Inspired Split Ring Resonator Structure Loaded Band Notched Antenna

A novel compact size ultrawide band planar antenna with band notched characteristics is present. The band rejection characteristic is achieved by loading a pair of metamaterial inspired rectangular split ring resonator (SRR) near the feed line and by etching the SRR slots on a radiating patch. The simulated and measured results reveal that the proposed antenna exhibits the impedance bandwidth over the ultrawide band (UWB) frequency range from 3.1 to 14 GHz with the voltage standing wave ratio less than 2 except for band stop bands at 3.29 to 3.7 GHz (WiMAX band), 3.7 to 4.10 GHz (C-band), 5.1 to 5.9 GHz (WLAN band), and 7.06 to 7.76 GHz (downlink X-band satellite communication), respectively. The proposed antenna fabricated on low-cost FR-4 substrate has compact size of 24 × 20 × 1.6 mm3. The simulation results are compared with measured results and demonstrate good agreement with stable gain over pass bands. The proposed antenna also exhibits dipole-like radiation pattern in E-plane and omni-directional pattern in H-plane. These results led to conclusion that the presented antenna is a suitable candidate for ultrawide band UWB applications with desired band notch characteristics

Metaheuristic and Machine Learning-Based Smart Engine for Renting and Sharing of Agriculture Equipment

Recently, many companies have substituted human labor with robotics. Some farmers are sharing different perspectives on the incorporation of technology into farming techniques. Some are willing to accept the technology, some are hesitant and bemused to adapt modern technology, and others are uncertain and are worried about the potential of technology to cause havoc and decrease yields. The third group prevails the most in the developed world, for lack of know-how, including translation of utility and, most significantly, the expense involved. A special Smart Tillage platform is established to solve the above issues. A smart-engine-based decision has been developed, which further uses classification and regression trees to shift towards decision-making. The decision is focused entirely on different input factors, such as type of crop, time/month of harvest, type of plant required for the crop, type of harvest, and authorised rental budget. Sitting on top of this would be a recommendation engine that is powered by deep learning network to suggest the escalation of a farmer from lower to higher category, namely, small to medium to large. A metaheuristic is one of the best computing techniques that help for solving a problem without the exhaustive application of a procedure. Recommendations will be cost-effective and suitable for an escalating update depending on the use of sufficient amends, practices, and services. We carried out a study of 562 agriculturists. Owing to the failure to buy modern equipment, growers are flooded by debt. We question if customers will be able to rent and exchange appliances. The farmers would be able to use e-marketplace to develop their activities