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A Design of Agricultural Robotics for the use of Sowing and Planting
Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE. -
Advanced Materials for Next-Generation Energy Storage Devices: A Focus on Efficiency and Cost Reduction
The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future. The Authors, published by EDP Sciences. -
Intelligent Time Management Recommendations Using Bayesian Optimization
This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. 2024 IEEE. -
Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People
Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency's uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently. 2021 IEEE. -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
An AI Approach to Pose-based Sports Activity Classification
Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models. 2021 IEEE -
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE. -
Hybrid Subset Feature Selection and Importance Framework
Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE. -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Financing for SDGs in India in Post Pandemic era - Challenges & Way forward
In 2015, a resolution known as Agenda 2030 was passed by United Nations General Assembly in which seventeen goals for Sustainable Development were laid down for global dignity, peace and prosperity. The post- pandemic era became full of uncertainties in pursuing those Sustainable Development Goals (SDGs) and its implementation became a challenge especially for the developing economies like India. The country is facing a tremendous gap in arranging for resources to meet the climatic changes and attaining the SDGs. India requires 170 billion dollars per year from 2015-2030 to fulfill the Sustainable Development Goals as per the estimation done by National Determined Contribution, a body setup after Paris agreement 2015 to monitor the efforts of the country towards reducing national emissions and adapting to climate change. There is a huge concern amongst the various agencies on exploring the ways to fill this financing gap especially after the economic slowdown seen in the post pandemic era. This research paper analyses the challenges imposed by the COVID 19 pandemic on financing for SDGs and also explores the options to mitigate them. The articles and research papers related to SDG financing are reviewed by the researchers to arrive at the above mentioned statements. This paper is an attempt to draw the attention of worldwide authorities towards this grim situation as sustainable finance is far from reality in India and requires immediate up scaling. The Electrochemical Society -
Investigating Personalized Learning Paths to Address Educational Disparities Using Advanced Artificial Intelligence Systems
This innovative study reimagines the role of Natural Language Processing (NLP) in individualized education by highlighting the critical need to incorporate cultural subtleties. While natural language processing (NLP) offers great potential for improving classroom instruction, current research frequently fails to account for the complex issues caused by cultural variation. This research fills a significant need by providing a novel framework for the detection and incorporation of cultural subtleties into individualized learning programs. Further research into common biases is driving the development of natural language processing models with greater cultural sensitivity and awareness, such as gender bias in Named Entity Recognition (NER) and sentiment bias in cultural preferences. In order to correct past biases and promote gender neutrality in educational content, the research makes use of an adaptive NER algorithm and a diverse training dataset. Similarly, to guarantee nuanced and fair sentiment evaluations, the study suggests regularly evaluating and retraining sentiment algorithms with datasets that represent multiple cultures. A Cultural Relevance Score of 0.9, Adaptive Content Embedding vectors [0.3, 0.6, -0.2.], and an impressive Cosine Similarity of 0.85 are some of the evaluation measures that highlight the effectiveness of the research. These measurements show encouraging gains, which confirms that the research might help make schools more welcoming and sensitive to different cultures. The research has the potential to revolutionize individualized education by making it more accessible and engagingfor students from all backgrounds. 2024 IEEE. -
Green Innovation for Sustainable Development
In recent years, organisations have notably taken up Green Innovation for sustainable development to maintain the customer base and keep the natural environment safe. Consumers are aware of the current environmental issues, such as global warming and the consequence of environmental pollution. As a result, organisations are demanding to craft green strategies and embryonic to advance holistic methods towards maximising shareholders' values. This paper attempts to provide valuable insights into the going green concepts and their association with the value creation in the automobile industry regarding the e-vehicle by examining the effects of green innovations in Mahindra Electric Mobility Limited, India towards the launching of Mahindra e2oPlus on the Reva platform. Furthermore, the authors analyse the performance of this innovation on the organisational financial performance with the help of event study methodology. 2022 IET Conference Proceedings. All rights reserved. -
Strategic Integration of HR, Organizational Management, Big Data, IoT, and AI: A Comprehensive Framework for Future-Ready Enterprises
This exploration paper proposes a comprehensive frame aimed at fostering unborn-ready enterprises through the strategic integration of Human coffers(HR), Organizational Management, Big Data, the Internet of Things (IoT), and Artificial Intelligence(AI). By synthesizing these critical factors, the frame seeks to optimize organizational effectiveness, enhance decision-making processes, and acclimatize proactively to evolving request dynamics. Through a methodical review of being literature and empirical substantiation, the paper delineates the interconnectedness of these rudiments and elucidates their collaborative impact on organizational performance and dexterity. likewise, it explores perpetration strategies and implicit challenges associated with espousing such an intertwined approach. This paper not only contributes to the theoretical understanding of strategic operation but also provides practical perceptivity for directors and directors seeking to navigate the complications of the contemporary business geography and place their associations for sustained success in a decreasingly digitized and competitive terrain. 2024 IEEE. -
Parallel Algorithm to find Integer k where a given Well-Distributed Graph is k-Metric Dimensional
Networks are very important in the world. In signal processing, the towers are modeled as nodes (vertices) and if two towers communicate, then they have an arc (edge) between them or precisely, they are adjacent. The least number of nodes in a network that can uniquely locate every node in the network is known in the network theory as the resolving set of a network. One of the properties that is used in determining the resolving set is the distance between the nodes. Two nodes are at a distance one if there is a single arc can link them whereas the distance between any two random nodes in the network is the least number of distinct arcs that can link them. We propose two algorithms in this paper with the proofs of correctness. The first one is in lines with the BFS that find distance between a designated node to every other node in the network. This algorithm runs in O(log n). The second algorithm is to identify the integer k, such that the given graph is k-metric dimensional. This can be implemented in O(log n) time with O(n2) processors in a CRCW PRAM. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Antecedent Factors in Adolescents Consumer Socialization Process Through Social Media
The research paper attempts to find the antecedent factors that influence in adolescents consumer socialization process through social media and its impact on family purchase. Consumer socialization of adolescents through social media has become a key indicator in the area of marketing because of predominant online interaction of consumer. Socialization process framework is adopted to investigate among 254 respondents. The results show there is positive influence of antecedent variables like age, social media and peer identification on Purchase Intention and the variable social media also influences Product Involvement in family decision making. The outcome of this research benefits the academicians and marketers to explore the impact of social media on adolescent in their family decision making. Springer Nature Switzerland AG 2020. -
Dynamic vibrational analysis on areca sheath fibre reinforced bio composites by fast fourier analysis
Natural fibre reinforced bio composites [6] are good alternative for conventional materials. Natural fibres are cheaper in cost, environmental friendly and biodegradable. In this project work the effect of varying fibre length is studied and Fast Fourier Technique is used for the analysis of dynamic frequency response. The naturally extracted areca sheath fibres are used as a reinforcement and epoxy L - 12 is used as polymer matrix. Fabrication is done by using hand lay-up method and compression molding technique at 100 - 110 bar pressure and 140 - 150C temperature. Each specimen is cured for 24 h and then test specimens were cut according to ASTM standards i.e., 150 X 150 mm in length and breadth. The dynamic frequency response of specimens with varying fibre length of 29, 27 and 25 mm and thickness 4, 3.5 and 2 mm is obtained by modal analysis. Finite Element Analysis for all specimens is carried out by ANSYS 14.5 and results are compared with the experimental values. These natural areca fibre reinforced polymer matrix composites are defined for particular applications based up on the mechanical and vibrational characteristics obtain from the experimental results. 2018 Elsevier Ltd. All rights reserved. -
Artificial Intelligence Technological Revolution in Education and Space for Next Generation
The goal of this research is to discover the various potential for the educational system using artificial intelligence (AI). The world today is dealing with AI in different sectors. This study specifically looked into the prospects for acquiring efficient and high-quality education for each student, automating administrative tasks, including regulating adaptive student support systems. AI has been leveraged and used in the education sector in various formations. AI initially took in the form of computers with the cognitive model, transformed to online learning, together with other technologies, the use of AI provides chatbots to perform instructors. Imagine you can access your classroom from anywhere at any time through an online learning system. These functionalities enable the education system to deal with the curriculum effectively. Using these facilities, teachers instruct the students to desire to achieve their goals efficiently. The primary aim of this article addresses the concepts in AI that serve to regulate and improve the overall quality of academic performance. The secondary aim of this article is to discuss AI involvement in the space domain. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Feminist Perspective on the Food and Gender based Marketing Narrative
Nutrition to the body is a basic element for sustenance and growth biologically, provided through food. This paper aims to understand why there is a difference between foods that are marketed gender-specifically to males and females separately. There have been a lot of participative changes in the household kitchen activities since the birth of the concept. However, certain things have continued to remain the same either as a result of tradition, preference, or systemic societal loop. This paper aims to categorically understand this patterned behaviour behind gender based food marketing and the consequent consumptions so as to find a more sustainable and inclusive approach for food marketing for the firms of this industry. The aim is also to shed light on the impact of such practices on the psychological level of the individual buyer that stems to form a pattern, creating a recurring practice out of habit, over internal choice. The Electrochemical Society -
SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.