Search

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

The objective of paper is to study a deep learning strategy for recognizing Indian Sign Language (ISL) using convolutional neural networks CNN. Sign language recognition has become a crucial tool for enhancing communication and accessibility for the deaf in India. ISL is a visual-gestural language used by the deaf population in India, and it is a visual-gestural language used by the deaf community in India. Gesture based communication Application is a characteristic language that involves various methods for articulation for correspondence in day-to-day existence. This paper presents a Programmed interpretation framework for token of manual letters in order in English communication via gestures. It manages pictures of uncovered hands, which permits the client to cooperate with the framework in a characteristic manner. In proposed system a deep learning model using CNN with ISL (Indian Sign Language) Dataset is used. The model consists of preprocessing the dataset to extract features from the image, then train a CNN to recognize the signs and gestures with audio. Experimental results show that the proposed approach is capable of achieving accuracy in recognizing ISL signs and gestures with accuracy of 0.868, recall 0.856, precision 0.863, and F-score is 0.884 respectively. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Snakebite poses a significant health threat in numerous tropical and subtropical nations, with around 5.4 million cases reported annually, which results in 1.8-2.7 million instances of envenomation, underscoring its critical impact on public health. The 'BIG FOUR' group comprises the primary committers responsible for most snake bites in India. Effective management of snakebite victims is essential for prognosis, emphasizing the need for preventive measures to limit snakebite-related deaths. The proposed initiative seeks to develop a transfer learning-based image classification algorithm using DenseNet to identify venomous and non-venomous snakes automatically. The study comprehensively evaluates the image classification results, employing accuracy, F1-score, Recall, and Precision metrics. DenseNet emerges as a potent tool for multiclass snake image classification, achieving a notable accuracy rate of 86%. The proposed algorithm intends to be incorporated into an AI-based snake-trapping device with artificial prey made with tungsten wire and vibration motors to mimic heat and vibration signatures, enhancing its appeal to snakes. The proposed algorithm in this research holds promise as a primary tool for preventing snake bites globally, offering a path toward automated snake capture without human intervention. These findings are significant in preventing snake bites and advancing snakebite mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appearance and routine activities, subtle indications of these diseases can manifest in the electrocardiogram (ECG) signals, often overlooked by standard interpretation. Current machine learning models have been ineffective in discerning these minor variations due to the irregular and subtle nature of changes in the ECG patterns. This paper uses a novel deep-learning approach to predict slight variations in ECG signals by fine-tuning the learning rate of a deep convolutional neural network. The strategy involves segmenting ECG signals into separate data sequences, each evaluated for unique centroid points. Utilizing a clustering approach, this technique efficiently recognizes minute yet significant variations in the ECG signal characteristics. This method is estimated using a specific dataset from SRM College Hospital and Research Centre, Kattankulathur, Chennai, India, focusing on patients' ECG signals. The model aims to predict the ordinary and subtle variations in ECG signal patterns, which were subsequently mapped to a pre-trained feature set of cardiovascular diseases. The results suggest that the proposed method outperforms existing state-of-the-art approaches in detecting minor and irregular ECG signal variations. This advancement could significantly enhance the early detection of cardiovascular diseases, offering a promising new tool in predictive medical diagnostics., Competing Interests: Declaration of competing interest The authors have no competing interests to declare that are relevant to the content of this article., (Copyright © 2024. Published by Elsevier Ltd.)

Residential waste is a substantial contributor to solid waste generation, which is approximately around 36.5 million tons annually in India. The waste created in households is not separated at the source. All waste is accumulated in a single waste bin and stashed in a nearby public waste bin, resulting in a massive amount of waste being dumped in landfills and also infused with other types of waste, causing environmental pollution. The core objective of this research is to develop a household waste segregator using the TensorFlow object detection model and Arduino microcontroller. The SSD MobileNet V2 model has been trained with a household dataset consisting of paper, plastic, metal, organic waste, glass, and one more additional empty class to detect whether waste is placed for detection or not. This proposed system can predict the waste class and segregate it into their specific dustbin with mean Average Precision (mAP) and recall of 86.5% and 88.3%, respectively. Waste segregation and recycling can reduce landfills, lower carbon footprints, increase recycling, recover value from garbage, and lower greenhouse gases emitted from waste. Segregation at the source will reduce the cost of the segregation process carried by the municipal solid waste management. [ABSTRACT FROM AUTHOR]

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images. With the help of object detection, the precise location of weeds from images can be achieved. The dataset is collected manually from a private farm in Gandhinagar, Gujarat, India. The combined approach of classification and object detection is applied in the proposed model. The Convolutional Neural Network (CNN) model is used to classify weed and non-weed images; further DL models are applied for object detection. We have compared DL models based on accuracy, memory usage, and Intersection over Union (IoU). ResNet-18, YOLOv3, CenterNet, and Faster RCNN are used in the proposed work. CenterNet outperforms all other models in terms of accuracy, i.e., 88%. Compared to other models, YOLOv3 is the least memory-intensive, utilizing 4.78 GB to evaluate the data. [ABSTRACT FROM AUTHOR]

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R 2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Deep learning models in recent times have shown promising results for solar energy forecasting. Solar energy depends heavily on local weather conditions, and as a result, typically hundreds of models are built, which need site and season-specific training. The model maintenance and management also become a tedious job with such a large number of models. Here, we are motivated to use transfer learning to accommodate local variations in the solar pattern over the available global pattern. It may also be noted that apparently transfer learning has been rarely/never used for solar forecasting. In this paper, we have proposed a bidirectional gated recurrent unit (BGRU) based model, which employs transfer learning for short-term solar energy forecasting. The said model yields better forecasting accuracy compared to site-specific models with a lower variance. It also takes 39.6% less parameters and 76.1% reduced time for training. The current literature suggests that selection of base scenario for transfer learning is an open problem and in this paper, we have also proposed an intuitive strategy for the same. The effectiveness of the same is established through empirical study. [ABSTRACT FROM AUTHOR]

The Indian solid waste management sector is experiencing fast growth and transition as a result of rising urbanization and industrial activity. This research study examines the landscape of solid waste management in India, focusing on the informal sector's involvement, market size, and trends. It also looks into the possibility of creating an online solid waste marketplace with a progressive web app (PWA) with the goal of optimizing waste management processes, increasing resource efficiency, and contributing to environmental sustainability. The purpose of this study is to create a new waste management system that uses the YOLO v7 item recognition model andThe project concentrated on crafting design prototypes for a progressive web app (PWA) and an administrative web interface for a solid waste marketplace in India. Utilizing Figma, these designs aim to streamline waste management processes, engage users effectively, and advocate for recycling initiatives. This effort signifies a pivotal move towards closing the gap in India's waste management infrastructure. It highlights technology's role in promoting environmental sustainability and responsible waste management practices nationwide. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables. Design/methodology/approach: A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. Findings: The T-bot's ADI's antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots. Practical implications: The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education. Originality/value: T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots. [ABSTRACT FROM AUTHOR]

Road crossing or at road side stray animals like cows and buffalos are usually observed in India. Such animals creates critical situations on roadways due to the collision. According to data obtained from the veterinary department of Nagpur municipal corporation approximately 3,127 cattle were injured and at least 30% of them died immediately and after a few days of hospitalization. This paper showcase a solar powered deep learning and IoT enabled early warning system which could detect the stray animals and send an alerts to the vehicles on road. The IoT cloud platform and dashboard delivers the information of accident prone zone. The trained stray animal detection model in the present system could predict 86% to 90% true positive results with 91% accuracy on real world camera feed. The 433 Mhz RF transmitter in the system is capable to send alert up to 110m. [ABSTRACT FROM AUTHOR]

In the domain of natural language processing, sentiment analysis is an important field for any review. Nowadays Indian languages are more popular for any product review. In North India, Hindi is the most widely used language. People having Hindi as their mother tongue can easily express their opinions and thoughts through that language. In the field of research, Hindi language has many challenges due to very less research work, limited data for analysis and less size of corpus data. Deep learning techniques are currently used for predicting feelings. Recurrent Neural Network (RNN), particularly the Long Short Term Memory (LSTM) and Convolution Neural Network (CNN), are two generally used deep learning approaches. Depending on the domain area of application, the strategies are utilized in combinations or as stand-alone procedures. This review paper emphases on the numerous flavours of deep learning approaches employed in various applications of sentiment analysis at the sentence and aspect levels. [ABSTRACT FROM AUTHOR]

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]

Data mining and machine learning algorithms' performance is degraded by data of high-dimensional nature due to an issue called "curse of dimensionality". Feature selection is a hot research topic where a subset of features are selected to reduce the dimensionality and thereby increasing the accuracy of learning algorithms. Redundant and irrelevant features are eliminated. Cervical cancer is most commonly occurring disease and driving reasons for untimely mortality among ladies worldwide especially in emerging low income nations like India. However, literature shows that the early identification and exact conclusion of cervical malignant growth can increase the survival chances. The disease does not show signs of its presence in the early stages of its growth. Automated classification and diagnosis of cervical cancer using machine learning and deep learning techniques is in high demand as it allows timely, accurate and regular study of the patient's health progress. Meta-heuristics algorithms provide a global problem independent optimal solution and applied on feature selection problem since decades. In spite of having a good number of literature, there is no survey on feature selection techniques applied to cervical cancer dataset. This paper presents a brief survey on meta-heuristic, its variants, hybrid meta-heuristic and hyper-heuristic techniques. This survey summarizes the feature selection techniques applied to the cervical cancer data to identify the research gap thereby guiding the researchers in the future research direction. The summary of categorization of the techniques such as nature-inspired or non-nature inspired and trajectory or population based is also highlighted. The survey provides a comparative literature review involving classical feature selection techniques and feature selection using metaheuristic algorithms for cervical cancer classification application. [ABSTRACT FROM AUTHOR]

The widespread growth in social media platforms provides a plethora of opportunities to enhance interaction and bring awareness about recent activities happening across the countries. Many people use social media to share their thoughts and opinions on societal and political issues. Nonetheless, some individuals misuse these platforms by posting toxic, hostile, and insulting comments. Hence, detecting and controlling such content at its earliest stage is crucial since its spread can harm social relations and negatively impact a person's life. In current scenarios, social media text consisting non-English languages is increasing due to active participation from multilingual societies. Of several non-English languages, Hindi English code-mixed is more prevalent in India. Most of the previous work to detect cyber aggression concentrates on English texts; therefore, there is high scope left to work on other languages such as Hindi English code-mixed. This paper has proposed a code-mixed hybrid embedding (CMHE) at the character and word level to capture similarly spelled and contextually related words. Furthermore, proposed embedding contributes significantly to the reduction of out of vocabulary words and capture words having similar polarity. After this, a deep learning framework based on CMHE, and a self-attention mechanism is proposed to retrieve significant features for classification. To evaluate proposed model, experiments were performed with two publicly available datasets: TRAC 2-2020 Hindi English code-mixed dataset (77.54% accuracy, 77.09% weighted average f1 score) and hate speech dataset (75.23% accuracy, 73.34% weighted average f1 score). The attained experimental results validate the effectiveness of proposed approach against the state-of-the-art. [ABSTRACT FROM AUTHOR]

In India, rice crops are very significant. Rice cultivation comprises several phases, and it is crucial to keep an eye on the crop's development to avoid any leaf diseases and to provide a good yield. To avoid yield loss, crop diseases need to be determined at the initial stage. Deep learning-based pre-trained CNN architecture is used in this study to identify rice leaf diseases. This paper discusses four different CNN architectures to classify and identify healthy and diseased leaves such as Brown spot, Hispa, and Leaf Blast. Initially, to avoid vanishing gradient problems that degrade the performance of the Network, ResNet34 and ResNet50 are used. Even though the CNN model performs the feature extraction, Self-attention with ResNet18 and ResNet34 architecture is utilized to improve the feature selection process. As a result of enhanced feature extraction, the accuracy of rice leaf disease identification and classification has improved. Finally, high accuracy of 98.54% is achieved with the proposed ResNet34 with self-attention architecture when compared to other CNN models used in this paper. In terms of multiclass classification, the proposed model offers improved outcomes when compared to state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In India and over the world, Cancer has become a deadly disease that is adversely affecting women from different age groups. According to a survey, one in every 30 women suffers from this deadly disease in their lifetime. This project was first thought of because of the increase in cases of breast cancer and if we can detect it at an early stage then there is an increased chance of it getting cured. This paper lays a foundation in making the detection of cancer automated from histopathological images using deep learning algorithms so that more and more people can get it diagnosed at an early stage and subsequently get cured. To enhance the capability of histopathology image classification, a powerful deep learning algorithm and a sizable, varied dataset are required. In this paper, we have proposed an automatic and precise histopathological image analytical method, to detect breast cancer at an early stage. Deep learning techniques have recently made significant strides and produced outstanding results in the fields of computer vision and image processing, which has encouraged many researchers to use this method for the categorization of histopathology images. [ABSTRACT FROM AUTHOR]

Background: This research adds to the growing body of work demonstrating the vital role of image categorization in the medical sector. The efficiency of illness diagnosis is being greatly enhanced using image classification. A brain tumor is a collection of abnormal cells in the brain. Diagnostic precision is required when looking for a tumor in the brain because even the smallest error in human judgement can have disastrous results. To get around this problem, the medical community has implemented an automated brain tumor segmentation system. A variety of methods are employed to segment a brain tumor, but the results are inconsistent. To improve the quality of MRI images, we present our findings in this paper. Deep learning methods for image segmentation and classification are discussed in this paper. Methods: In this paper we a very robust deep learning method for image segmentation and classification. For image classification, we are employing the enhanced faster R-CNN method. The ResNet50 model is used to differentiate between tumor and non-tumor images. The next step in this framework is to use Deep Residual UNET for segmentation. RESUNET is an FCNN that maximizes efficiency. The proposed method works well in terms of its ability to find and classify things accurately. Results: The accuracy rate for identifying tumours in MRI scans using the proposed technique is 94.23%. Using transfer learning with Resnet50 as the base model and the use of discriminative learning rates, our model achieved superior results than other recent models. Conclusion: Within the scope of this study, we have integrated the residual networks with the U-Net to make the network stronger. This strategy improves the efficiency of classification and segmentation tools. To achieve a higher level of accuracy, the model may be trained further, or additional data may be applied in the training process. [ABSTRACT FROM AUTHOR]

Tea plant cultivation plays a significant role in the Indian economy. The Tea board of India supports tea farmers to increase tea production by preventing various diseases in Tea Plant. Various climatic factors and other parameters cause these diseases. In this paper, the image retrieval model is developed to identify whether the given input tea leaf image has a disease or is healthy. Automation in image retrieval is a hot topic in the industry as it doesn’t require any form of metadata related to the images for storing or retrieval. Deep Hashing with Integrated Autoencoders is our proposed method for image retrieval in Tea Leaf images. It is an efficient and flexible way of retrieving Tea Leaf images. It has an integrated autoencoder which makes it better than the state-of-the-art methods giving better results for the MAP (mean average precision) scores, which is used as a parameter to judge the efficiency of the model. The autoencoders used with skip connections increase the weightage of the prominent features present in the previous tensor. This constitutes a hybrid model for hashing and retrieving images from a tea leaf data set. The proposed model will examine the input tea leaf image and identify the type of tea leaf disease. The relevant image will be retrieved based on the resulting type of disease. This model is only trained on scarce data as a real-life scenario, making it practical for many applications. [ABSTRACT FROM AUTHOR]

The health organisation has suffered from the lack of diagnosis support systems and physicians in India. Further, the physicians are struggling to treat many patients, and the hospitals also have the lack of a radiologist especially in rural areas; thus, almost all cases are handled by a single physician, leading to many misdiagnoses. Computer aided diagnostic systems are being developed to address this problem. The current study aimed to review the different methods to detect pneumonia using neural networks and compare their approach and results. For the best comparisons, only papers with the same data set ChestXray14 are studied. [ABSTRACT FROM AUTHOR]

Even after scavenging the existence of mankind for the past year, the wrath of CoVID-19 is yet to die down. Countries like India are still getting haunted by the devastating conundrum, with coronavirus ripping through its citizens in the concurrent second wave. The surge of cases has prompted rapid intervention, with medical authorities pushing it to the limit to curve a roadblock to its aggressive growth. But, even after effortless work, human intervention remains slow and insufficient. Furthermore, relevant testing methodologies have shown weakness while detecting threats, with the recent growth of post-Covid complexities, thereby leaving a painful mark. This as such created a major requirement for technological advancements, which can cater to the mass. The growth of computational prowess in the past decade made the field of Deep Learning a major contributor in curving out algorithms to solve this. Adding to the excellent foundation of Deep Learning, this paper, proposes a novel CoWarriorNet model for rapid detection of CoVID-19, via chest X-ray images, which adds in an extra layer of precision and confirmation in the detection of cases in both pre-Covid and post-Covid conditions. The proposed classification model curves out an excellent accuracy of 97.8%, with the major eye-candy being the sensitivity rate of 0.99 when detecting CoVID-19 cases. This model introduces a new concept of Alpha Trimmed Average Pooling, which along with the novel architecture adds a subtle touch to its high efficiency, thereby giving a much-needed solution to the medical experts. The two-mouthed architecture provides the added benefit of a confidence score, deducing human aid in case of discrepancy. [ABSTRACT FROM AUTHOR]

Big data analytics involve a systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involves grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It applies feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. The system also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. The overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 82%, and it can be increased as a future work by including deep learning with time-series grape leaf images. [ABSTRACT FROM AUTHOR]

COVID-19 has laid an impact on every sector of the world. Howsoever severe, vaccines have acted as the sole source of a protective guard to prevent the further spread of the COVID-19 pandemic. In this research, the authors broadly focus on the trends in the vaccination drive of India. The paper revolves around a prediction and evaluation approach, which depending on the past and the current trends of daily vaccinations, obtain comparable results using a self-built recurrent neural network of LSTM layers for this study on time series evaluation. Through the neural network, the study predicts the exact vaccination figures likely to be achieved 1 year after vaccine introduction in the Indian subcontinent. The gathered data from January 16, 2021, until September 30, 2021, follow effective visualization of how the model outputs resemble the vaccination numbers for October 2021 and the predictions until January 16, 2022. Finally, the paper follows an extensive data analysis keeping in mind, the analogy of the number of COVID-19 cases and deaths before and after the vaccination system was centralized, to prove how sustainable the framework has been so far. [ABSTRACT FROM AUTHOR]

Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%. [ABSTRACT FROM AUTHOR]

About 70% of India's economy is involved in the agriculture sector to live their lives and contributed to the GDP of the country. The Crop yield information along with the environmental change estimate will be useful for the agriculturalist to decide on price policies prior to harvesting the food source. It establishes a requirement for the prediction model, which precisely determines the harvest conditions, crop varieties, and agricultural yield. In literature, numerous crop prediction methods were devised to estimate crop production in the agricultural field & each technique has its potential in terms of yield forecasting. This review article provides a detailed analysis of the utilized approaches in the literature for the prediction of crop production as well as a discussion on the identification of concerns related to the yield gaps of crops. The discussed approaches were classified based on the application of different strategies, such as Machine learning methods, Deep learning methods, Data mining techniques, vegetative indices, fuzzy logic, and hybrid methods. The study was analyzed based on performance metrics, year of publication, datasets employed, software used for experimentation, and performance attained using various methods and highlights the research gaps of the respective method along with the future direction. [ABSTRACT FROM AUTHOR]

Early detection and identification of plant diseases from leaf images using machine learning is an important and challenging research area in the field of agriculture. There is a need for such kinds of research studies in India because agriculture is one of the main sources of income which contributes seventeen percent of the total gross domestic product (GDP). Effective and improved crop products can increase the farmer's profit as well as the economy of the country. In this paper, a comprehensive review of the different research works carried out in the field of plant disease detection using both state-of-art, handcrafted-features- and deep-learning-based techniques are presented. We address the challenges faced in the identification of plant diseases using handcrafted-features-based approaches. The application of deep-learning-based approaches overcomes the challenges faced in handcrafted-features-based approaches. This survey provides the research improvement in the identification of plant diseases from handcrafted-features-based to deep-learning-based models. We report that deep-learning-based approaches achieve significant accuracy rates on a particular dataset, but the performance of the model may be decreased significantly when the system is tested on field image condition or on different datasets. Among the deep learning models, deep learning with an inception layer such as GoogleNet and InceptionV3 have better ability to extract the features and produce higher performance results. We also address some of the challenges that are needed to be solved to identify the plant diseases effectively. [ABSTRACT FROM AUTHOR]

Kidney cancer is the most common type of cancer, and designing an automated system to accurately classify the cancer grade is of paramount importance for a better prognosis of the disease from histopathological kidney cancer images. Application of deep learning neural networks (DLNNs) for histopathological image classification is thriving and implementation of these networks on edge devices has been gaining the ground correspondingly due to high computational power and low latency requirements. This paper designs an automated system that classifies histopathological kidney cancer images. For experimentation, we have collected Kidney histopathological images of Non-cancerous, cancerous, and their respective grade of Renal Cell Carcinoma (RCC) from Kasturba Medical College (KMC), Mangalore, Karnataka, India. We have implemented and analyzed performances of deep learning architectures on a Field Programmable Gate Array (FPGA) board. Results yield that the Inception-V3 network provides better accuracy for kidney cancer detection as compared to other deep learning models on Kidney histopathological images. Further, the DenseNet-169 network provides better accuracy for kidney cancer grading as compared to other existing deep learning architecture on the FPGA board. [ABSTRACT FROM AUTHOR]

Speech Emotion Recognition (SER) is very crucial in enriching next generation human machine interaction (HMI) with emotional intelligence capabilities by extracting the emotions from words and voice. However, current SER techniques are developed within the experimental boundaries and faces major challenges such as lack of robustness across languages, cultures, age gaps and gender of speakers. Very little work is carried out for SER for Indian corpus which has higher diversity, large number of dialects, vast changes due to regional and geographical aspects. India is one of the largest customers of HMI systems, social networking sites and internet users, therefore it is crucial for SER that focuses on Indian corpuses. This paper presents, cross corpus SER (CCSER) for Indian corpus using multiple acoustic features (MAF) and deep convolution neural network (DCNN) to improve the robustness of the SER. TheMAF consists of various spectral, temporal and voice quality features. Further, Fire Hawk based optimization (FHO) technique is utilized for the salient feature selection. The FHO selects the important features from MAF to minimize the computational complexity and improve feature distinctiveness based in inter class and inter class variance of the features. The DCNN algorithm provides the better correlation, higher feature representation, better description of variation in timbre, intonation and pitch, superior connectivity in global and local features of the speech signal to characterize the corpus. The outcomes of suggested DCNN based SER is evaluated on Indo-Aryan language family (Hindi and Urdu) and Dravidian Language family (Telugu and Kannada). The proposed scheme results in improved accuracy for the various cross corpus and multilingual SER and out performs the traditional techniques. It provides an accuracy of 58.83%, 61.75%, 69.75% and 45.51% for Hindi, Urdu, Telugu and Kannada language for multilingual training. [ABSTRACT FROM AUTHOR]

Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions. [ABSTRACT FROM AUTHOR]

In India, a significant portion of the annual crop is lost due to plant diseases. It is required to monitor agriculture's growth and health status of plants to help farmers take prompt actions regarding any plant disease. An automatic plant disease classification algorithm is necessary to identify and prevent plant diseases at an early stage. This paper aims to classify plant diseases using a lightweight transfer learning model using only a few samples. Few-shot classification aims to identify unseen classes while training with small labeled data. The proposed work uses an aggregated loss function formed by the combination of triplet loss and cross-entropy loss with MobileNetV2 as a base model for the effective classification of plant disease using small samples. Two publicly available datasets (PlantVillage having 54,303 leaf samples and Plantdoc having 2598 Leaf samples) were considered for the evaluation of the proposed work. Different domain splits were considered to divide the dataset into the source, and target domains, and a large quantum of experimentation is conducted on the target dataset considering various sample sizes. For the analysis of plantvillage dataset, four domain splits were considered, and it is found that an average improvement in accuracy, when compared with recent work, is 1.49% for split-1, 16.25% for split-2, 2.9% for split-3, and 2.1%for split-4 using the proposed aggregated loss and the lightweight transfer learning model for the target domain data (K-ways, N-shot). Two domain splits were considered for the plantdoc dataset, and it gives an accuracy of around 81% using 30 samples and more than 40% using only 1 sample. The proposed work is compared with several state-of-the-art works using different evaluation metrics like, loss functions, execution time, model size, and model parameters. [ABSTRACT FROM AUTHOR]

Nowadays, for controlling the organizations' environmental impact and attaining performance enhancement in industries, green supply chain management (GSCM) plays a significant role. This work considers four major drivers and five primary dimensions for the deployment of GSCM systems in industries. The five primary dimensions are consumer intrusiveness, restructuring quality of supply chain, risk and security consciousness, and urbanization in the region leather industry, and the four major drivers are the effectiveness, features, significance, and limitations of the restructured GSCM system. Additionally, this paper investigates the primary factors that are used for deploying the GSCM system in leather industries located in the northern part of Tamil Nadu. Initially, the survey questions are prepared based on the concept and distributed to the authorities of industries. For the survey questions provided to various industries, three leather industries properly answered all the questions. Each question is mandatory. Then, the collected data were analyzed using the deep belief network (DBN) to measure the impact of primary drivers in implementing the GSCM system in industries. The performance of the proposed system is analyzed with various existing methods in terms of accuracy, precision, f-measure, etc. The proposed framework's implementation has implications for increasing organizational efficiency, lowering costs, minimizing waste, and encouraging green culture among personnel. This study will aid in the development of policies and a better knowledge of how to implement green innovation methods., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc. [ABSTRACT FROM AUTHOR]

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. The occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers' income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model's performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi. [ABSTRACT FROM AUTHOR]

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

In countries like India, diseases in plants are a major concern in the agricultural sector. Crop loss due to diseases led to reduction in the quality and quantity of agricultural products. This also leads to economic losses. Hence, timely monitoring of plants is necessary. But monitoring diseases in large fields is a difficult task. To overcome this problem, effective management strategies should be taken to control diseases in plants. It can be done by acquiring data for disease identification and automation of the recognition of diseases. Deep learning is of great use in this area using the principles of object detection. In this paper, we are using YOLOv3 (you only look once) to determine the diseases in plants, based on the symptoms seen on the leaves. The advantage of using YOLOv3 is that multiple diseases can be detected on the image of a single leaf. An important feature of YOLOv3 is that it can detect small disease spots seen on the leaves. Here, we are concentrating on the bacterial spot disease seen on the bell pepper plant. The identification results show a mean average precision of 90%. [ABSTRACT FROM AUTHOR]

Text classification involves organizing textual information into predefined classes, a task which is particularly useful in domains like sentiment analysis, spam detection, and content labeling. In India, where a massive amount of information is generated daily through newspapers and social media, Hindi is one of the most widely used and spoken languages. However, there is limited research on Hindi text classification and, particularly, regarding Hindi news classification. This paper presents a research study to classify Hindi news articles published in Hindi-language newspapers in India by using and comparing various Machine Learning (ML) and Deep Learning (DL) algorithms. To prepare the textual news data for classification, pre-processing and feature engineering techniques, such as count vectorizer, Tf-Idf vectorizer and Doc2Vec, were used and applied to convert texts into vectors. This pre-processing step on the textual data was very challenging due to the presence of multimodal words, conjunctions, punctuation, and special characters in Hindi texts. The study considered Hindi news headlines from predetermined categories (Science, Sports, Entertainment and Business) and, among the different ML and DL models tested and evaluated, Linear Regression with Doc2Vec vectorizer and SGD classifier with Tf-Idf vectorizer produced best accuracies of 97.04% and 96.59%, respectively. The best performing DL model was found to be the Bi-LSTM with an accuracy of approximately 97% on the testing data. [ABSTRACT FROM AUTHOR]

Background and Objective: The ever-mutating COVID-19 has infected billions of people worldwide and seriously affected the stability of human society and the world economic development. Therefore, it is essential to make long-term and short-term forecasts for COVID-19. However, the pandemic situation in different countries and regions may be dominated by different virus variants, and the transmission capacity of different virus variants diversifies. Therefore, there is a need to develop a predictive model that can incorporate mutational information to make reasonable predictions about the current pandemic situation., Methods: This paper proposes a deep learning prediction framework, VOC-DL, based on Variants Of Concern (VOC). The framework uses slope feature method to process the time series dataset containing VOC variant information, and uses VOC-LSTM, VOC-GRU and VOC-BILSTM prediction models included in the framework to predict the daily newly confirmed cases., Results: We analyzed daily newly confirmed cases in Italy, South Korea, Russia, Japan and India from April 14th, 2021 to July 3rd, 2021. The experimental results show that all VOC-DL models proposed in this paper can accurately predict the pandemic trend in the medium and long term, and VOC-LSTM model has the best prediction performance, with the highest average determination coefficient R2 of 96.83% in five nations' datasets. The overall prediction has robustness., Conclusions: The experimental results show that VOC-LSTM is the best predictor for such a series of data and has higher prediction accuracy in the long run. At the same time, our VOC-DL framework combining VOC variants has reference significance for predicting other variants in the future., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interests., (Copyright © 2022. Published by Elsevier B.V.)

Investors at well-known firms are increasingly becoming interested in stock forecasting as they seek more effective methods to predict market behavior using behavioral finance tools. Accordingly, studies aimed at predicting stock performance are gaining popularity in both academic and business circles. This research aims to develop a knowledge graph-based model for representing stock price movements using fundamental ratios of well-known corporations in India. The paper uses data from 15 ratios taken from the top 50 companies according to market capitalization in India. The data were processed, and different algorithms were used to extract tuples of knowledge from the data. Our technique involves guiding a domain expert through the process of building a knowledge graph. The scripts of the proposed knowledge representation and data could be found here: GitHub. The work can be integrated with a deep learning model for explainable forecasting of stock price. [ABSTRACT FROM AUTHOR]

Rice is a staple food for roughly half of the world's population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India's population relies on agriculture in some way and that agribusiness accounts for about 17% of India's GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers' manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases. [ABSTRACT FROM AUTHOR]

Image denoising is an important preprocessing step in low-level vision problems involving biomedical images. Noise removal techniques can greatly benefit raw corrupted magnetic resonance images (MRI). It has been discovered that the MR data is corrupted by a mixture of Gaussian-impulse noise caused by detector flaws and transmission errors. This paper proposes a deep generative model (GenMRIDenoiser) for dealing with this mixed noise scenario. This work makes four contributions. To begin, Wasserstein generative adversarial network (WGAN) is used in model training to mitigate the problem of vanishing gradient, mode collapse, and convergence issues encountered while training a vanilla GAN. Second, a perceptually motivated loss function is used to guide the training process in order to preserve the low-level details in the form of high-frequency components in the image. Third, batch renormalization is used between the convolutional and activation layers to prevent performance degradation under the assumption of non-independent and identically distributed (non-iid) data. Fourth, global feature attention module (GFAM) is appended at the beginning and end of the parallel ensemble blocks to capture the long-range dependencies that are often lost due to the small receptive field of convolutional filters. The experimental results over synthetic data and MRI stack obtained from real MR scanners indicate the potential utility of the proposed technique across a wide range of degradation scenarios. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) is one of the major air pollutants and is an important parameter for measuring air quality levels. High concentrations of PM 2.5 show its impact on human health, the environment, and climate change. An accurate prediction of fine particulate matter (PM 2.5) is significant to air pollution detection, environmental management, human health, and social development. The primary approach is to boost the forecast performance by reducing the error in the deep learning model. So, there is a need to propose an enhanced loss function (ELF) to decrease the error and improve the accurate prediction of daily PM 2.5 concentrations. This paper proposes the ELF in CTLSTM (Chi-Square test Long Short Term Memory) to improve the PM 2.5 forecast. The ELF in the CTLSTM model gives more accurate results than the standard forecast models and other state-of-the-art deep learning techniques. The proposed ELFCTLSTM reduces the prediction error of by a maximum of 10 to 25 percent than the state-of-the-art deep learning models. [ABSTRACT FROM AUTHOR]