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In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of images. The objective of this paper is to highlight the steps required for creating a new dataset. This necessitated a review of the existing dataset models prior to creating the new dataset. A sequential convolutional model for detecting objects on a new dataset is also presented. The process of creating a new image captioning dataset and the gained insights are described. [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]

The study is undertaken to examine international trade and tourism nexus in case of India and SAARC countries. To achieve the objective, panel cointegration is employed over the period of 22 years (1997 to 2018). The results from both first and second generation cointegration tests indicated the existence of long run equilibrium among tourism, export, and import. Albeit there are differences between short-term and long-term associations, the findings from autoregressive distributed lag approach indicates strong long-term linkages between trade and tourism. A possible channel of exports exerting a positive influence on tourist arrival is that the products in foreign markets increase the recognition and image of a country eventually leading to more tourists. Moreover, increased exports are expected to increase business trips further increasing the leisure trips and hence a strong linkage between export and tourism. On the other hand, a possible explanation of positive effect of imports on tourism could be that the destination countries which provide tourists with products according to their home taste encourage tourists to travel to these countries. The findings provided in the paper have strong implications for expanding India's tourism footprint in the region. [ABSTRACT FROM AUTHOR]

In India, electoral voting is done using electronically operated voting machines. Currently, the identity of the voter is validated by non-biometric process. Implementation of technology through computerized process has scope for using biometrics to validate voter's identity. Physiological or behavioral features of human are used as a biometric for personal identification. There is a large dataset of biometric patterns available, and many software systems have been developed and implemented, for recognition of face, hand shape, fingerprint, palm, iris, etc. The system proposed in this paper is implemented using embedded technology to utilize finger-vein image recognition. The system also has a MATLAB section, which will capture the vein in finger of the voter and their sample is registered to the controller. In order to cast a vote, the captured finger vein image must match the image already stored in the database along with the voter's RFID card. It the image identification failed to match with the authorized person, the system will send alert to authorities. [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]

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]

Crop pests and diseases are major threats to food security globally. The mung bean (Vigna Radiata) is one of the leading crops in India. A large part of the population in India is completely dependent on mung bean. So, high production efficiency for the mung bean is required, which does not happen due to the excessive damage from pests and diseases. Recently, with the advancement of Deep Learning techniques, remarkable performance has been achieved in the field of image classification by employing Convolutional Neural Networks (CNNs). This brings a lot of promise in the field of pest and disease identification by effective image classification. In this paper, we have proposed a novel deep learning-based technique to identify the mung bean pest and disease. In order to handle the limitation arising due to less number of mung bean crop images for the purpose of training, we have adopted transfer learning, which is able to generate a very promising result for quick and easy pest and disease detection. The developed model has successfully recognized 6 different types of mung bean diseases and 4 types of pests out of healthy and affected leaves collected in different seasons. Based on the experiments conducted, the proposed smartphone-based deep learning model for the mung bean pest and disease detection has achieved an average accuracy of 93.65%. [ABSTRACT FROM AUTHOR]

As the process of urbanization intensifies, a city expands from its center to its periphery. Due to the rise in human activities and population of a city, the physical limit gets stretched to provide housing along its periphery, resulting in extensive irreversible land cover alteration in the peri-urban areas. This chaotic and extensive urbanization in a peri-urban environment drives up the concerns about assessing and encouraging urban growth in a planned manner. The paper in a similar vein aims to determine peri-urban area and examine land cover change in and around the Hisar, an Indian city, between 2001 and 2021 by using geospatial techniques. The peri-urban zone of the hisar city has been delineated by applying the vector-based overlay analysis of different variables. The urban expansion was analysed with the help of the data acquired from image classification and by applying Shannon entropy model. The overall pattern of change in land uses shows that the area under built-up has increased by 99.17 percent by leaps and bounds. On the other hand, farmland, vegetation, water bodies and open spaces, are witnessing a sharp decline of 6.38 percent. The findings suggest that the built-up areas have substantially doubled from 4,965.12 ha to 9,889.2 ha throughout the study period. The scattered and random urbanization on the city outskirts is confirmed by the value of Shannon's entropy which increased from 1.93 in 2001 to 1.98 in 2021. The findings of the study would assist the administration and town planners in recognising the massive urban sprawl in the city and enable them to execute more effective approach to develop the peri-urban area. [ABSTRACT FROM AUTHOR]

Municipal solid waste generation in India is increasing as a result of rapid urbanisation and a growing population. Due to gaps in the existing waste collection, treatment and recovery methods, large-scale unscientific disposal and lesser waste recovery have been observed. Insufficient source segregation, unorganised efforts, less awareness and indifference towards malpractice, and a lack of strict implementation were identified as the causes through literature review, surveys and interviews, Therefore, there is a need for a system of interventions, which organises efforts toward waste management and reduces the tolerance for source segregation errors. This paper aims to provide a solution that increases community participation and prevents the generation of mixed waste to ensure waste recovery. The proposed framework integrates municipal solid waste, human elements, and technology into an intelligent app-based system. This includes a smart bin that segregates waste at the source through image classification and an application for communication and data collection. [ABSTRACT FROM AUTHOR]

Deep-Learning and image processing have shown excellent performance in automated fish image classification and recognition task in recent years. In this research paper, we have come up with a novel deep-learning method based on CNN features extracted from deeper layer of a pretrained CNN architecture for automatic classification of eleven (11) indigenous fresh water fish species from India. We have utilized top three layers of a pretrained Resnet-50 model to extract features from fish images and an "ones for all SVM" classifier to train and test images based on the CNN features. This paper reports an exceptional result in overall classification performance on Fish-Pak dataset and on our own dataset. The proposed framework yields overall classification accuracy, precision and recall of 100% on our own data and a maximum of 98.74% accuracy on Fish-Pak dataset which is the best till date. [ABSTRACT FROM AUTHOR]

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. [ABSTRACT FROM AUTHOR]

Agriculture being the pillar of the economy for a developing country like India has a vital role in the survival of living beings on earth. Wheat is the most widely consumed grain on the planet. Deep learning is an evolving technology that is having a significant effect in the field of agriculture, assisting farmers in modernizing their operations. One such application is the identification of plant diseases using image classification which is necessary for long-term agriculture sustainability. Wheat plants are susceptible to a variety of fungal diseases. Hence early identification of diseases of crops like wheat and rice that are staple food of people in many countries is critical. Using deep learning algorithms such as CNN, this proposed system aims to predict wheat diseases. We are introducing a deep learning-based model for image classification to predict wheat diseases. Previous approaches used machine learning algorithms for a general dataset that included all types of crop diseases. To achieve better precision, we built our own dataset and combined it with existing similar datasets to account for 4 major classes of wheat diseases. The dataset consists of 700 images of wheat plants. Based on the input, our system determines if the plant is healthy or diseased so that precautionary measures can be taken to prevent losses in wheat cultivation, which could lead to food shortages. [ABSTRACT FROM AUTHOR]

This study performs the time series analysis of agriculture land in the Nainital District of Uttarakhand, India. The study utilizes Landsat satellite images for the classification of agriculture and non-agriculture land over a time duration of 21 years (2000–2021). Landsat 5, 7 and 8 satellites data have been used to classify the study area with Random Forest classifier. The Landsat satellite images are processed using the Google Earth Engine (GEE) platform. The selection of Random Forest classier has been based on a comparative analysis among Random Forest (RF), Support Vector Machines (SVM) and Classification and Regression Trees (CART). Overall accuracy, user accuracy and producer accuracy and Kappa coefficient has been evaluated to determine the best classifier for the study area. The overall accuracy for RF, SVM and CART for the year 2021 is 96.38%, 94.44% and 91.94% respectively. Similarly, the Kappa coefficient for RF, SVM and CART was 0.96, 0.89, 0.81 respectively. The classified images of Landsat in agriculture and non-agriculture area over a period of 21 years (2000–2021) shows a decrement of 4.71% in agriculture land which is quite significant. This study has also shown that the maximum decrease in agriculture area in last four years, i.e., from 2018 to 2021. This kind of study is very important for a developing country to access the change and take proper measure so that flora and fauna of the region can be maintained. [ABSTRACT FROM AUTHOR]

The agriculture sector is one of the major sectors in India. India is well known for the production of various varieties of spices, fruits, vegetables, herbs, etc. Along with the pollution, the diseases that are affecting plants are increasing and there are various reasons for this. Tomato is one of the high-demand crops in the market and is produced in large quantities. There are many diseases that tomatoes get affected by because of the virus, fungus, bacteria, etc. In this project, we proposed a model to identify the diseases of tomato plants using images of tomato plant leaves. Our main goal is to develop a good model with decent accuracy and a mobile application that works with or without the internet for users, especially farmers. The Convolution Neural Network-based approach is used to create the model for this project. This proposed system model gives 98 % accuracy and that model is converted to the TF Lite model which is used in the application. This application can precisely predict the disease of the tomato leaf and suggest the treatment for it. [ABSTRACT FROM AUTHOR]

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season's growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes. [ABSTRACT FROM AUTHOR]