Volume 11 | Issue 5 |

 Abstract

Sustainability in Supply Chain Management (SSCM) has emerged as a paramount concern for businesses globally, prompted by escalating environmental crises and societal pressure for ethical business practices. This research paper undertakes a comprehensive and systematic review of the extant literature on SSCM, encompassing multidisciplinary studies from the past two decades. The goal is to distil the salient themes, theoretical frameworks, methodologies, findings, and future research directions, thereby rendering a panoramic view of the field. The review reveals SSCM as a complex and nuanced arena, influenced by a multiplicity of factors including regulatory policies, technological advancements, stakeholder pressures, and organizational culture. Despite substantial progress, significant knowledge gaps persist, particularly concerning the global South and the integration of Industry 4.0 technologies. The paper concludes with recommendations for future research and practice.

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 Keywords

Sustainability, Supply Chain Management, Systematic Literature Review, Global Context, Industry 4.0, Stakeholder Engagement, Regulatory Policies, Organizational Culture.

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This paper proposes a wireless charging system for electric vehicles (EVs) that aims to address the limitations of traditional plug-in charging systems. The wireless charging system utilizes inductive power transfer technology to transfer energy wirelessly between the charging pad and the EV battery. The system incorporates efficient power transfer through mutual induction and a control system for safe and reliable operation. The system's key features include automatic alignment and power transfer control, fault detection, and thermal management. The study also evaluates the system's efficiency, electromagnetic compatibility, and electromagnetic interference. The proposed wireless charging system offers a convenient and safe charging solution for EVs, reducing the need for physical connection between the charger and the vehicle, and enabling efficient charging while on the go.

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 Keywords

Mutual induction, Electric vehicles, Static wireless charging.

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Stock market forecasts have always been the focus of experts in this and related fields. Over the years, many modern techniques have been used in conjunction with previously available statistical models to find better predictive techniques. Among modern technologies, machine learning and specifically artificial intelligence holds the greatest share of predictive models on the market. Compared to other techniques, deep learning techniques showed better results in modelling market movements. Some techniques have been tried and tested individually but with unsatisfactory results, these techniques include automatic feature extraction, time series prediction, recurrent neural network (RNN), etc. However, hybrid frameworks with multiple inputs and based on deep learning methods such as RNN and LSTM (Long Short-Term Memory) have not been studied much. We propose a recurrent neural network (RNN)-based framework that combines with long-short-term memory (LSTM) to predict the stock market closing price. The RNN-LSTM framework extracts features from a rich feature set and applies time series modelling to predict what will happen next. The set includes raw price data for the target index as well as foreign indices, technical indicators, exchange rates, and commodity price data, all selected by protocol and price configurations. known in the industry. Now let's come to the LSTM (Long Short-Term Memory) part, which is known for its series forecasting. LSTM is greatly helpful in time series forecasting. A correct price forecast will bring huge benefits to users of our framework/model. Successfully predicting the future price of a stock can bring substantial profits.

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 Keywords

Long Short-Term Memory, Recurrent Neural Network (RNN), Deep Learning, Machine Learning, Artificial Intelligence.

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The increasing adoption of electric vehicles (EVs) has prompted the development of efficient charging infrastructure and innovative vehicle-to-home (V2H) systems. This project presents a solar-based bi-directional electric vehicle charger that enables a V2H system, allowing the transfer of energy between the EV and the home. The proposed charger integrates solar power generation with bidirectional power flow capability, enabling the EV to not only charge from the solar panels but also supply power back to the home during peak demand or in emergency situations. The system incorporates power electronics converters and energy management strategies to ensure efficient and reliable operation. Moreover, the charger is designed to meet safety standards, providing flexibility and grid support. The Solar Based Electric vehicle Charger's efficiency, reliability, and ease of use also make it an attractive option for EV owners and operators, providing a cost-effective and convenient charging solution. As the world continues to shift towards renewable energy sources, the Solar Based Electric vehicle Charger is poised to play a crucial role in shaping the future of transportation and energy.

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 Keywords

Bi-directional, Vehicle to home, Vehicle to grid, Converters

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One disease at a time is the focus of many of the machine learning models for healthcare analysis currently in use. For example, one analysis is done for cancer, another for heart disease, etc. Multiple diseases cannot be predicted using the same framework by a single analysis. This study proposes a system to forecast various diseases using the Flask Application programming interface (API). The approach for examining diabetes, heart disease, kidney, liver, and cancer was proposed in this work. Numerous disease analyses were implemented using TensorFlow, the Flask API, and machine learning techniques. Python dataset is used to save the model behavior and to load the dataset file as necessary. The importance of this article analysis lies in the fact that all the contributing elements to the illness are taken into account while analyzing it, allowing for the identification of the disease's complete range of potential effects. For example, a lot of the existing techniques for analyzing diabetes took into account a few factors like age, sex, BMI, and insulin. However, in contrast to the existing model, our proposed system takes into account additional variables like the number of pregnancies, blood glucose levels, skin thickness, heart rate, and family history of diabetes. The behavior of the finished model will be captured in a Python dataset file, and the Flask API will be developed. When using this API, the disease's parameters must be sent along with the disease's name. The Flask API will call the pertinent model and then return the patient's status. The goal of this project is to look at as many diseases as we can in order to monitor patients' status and provide them warnings, when necessary, in order to lower the death rate

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 Keywords

Machine Learning, Flask API, Convolutional Neural Network, XG-Boost

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Creative Commons Attribution 4.0 and The Open Definition

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In this paper Wireless charging of E-vehicle the wireless charging can eliminates the need for physical cables and connectors, making the charging process more convenient and hassle-free. In this project work, solar photovoltaic based Class-E inverters are used because the class E inverter is employed to provide high efficiency, frequency and low-cost operation. The results show that the proposed system provides high power transfer efficiency and can effectively charge electric vehicles wirelessly using renewable energy sources. The system is operating entirely through automation, there will be an zero manual controls compare than to existing prototype. By using sensor and switching devices the automation is carried out with the help of microcontrollers.

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 Keywords

Solar PV, Battery Swap, Class E Inverter, Voltage Sensors, Automation, IOT (Internet of Things)

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 Abstract

The competition among investors to secure the best talent in the rapidly expanding field of information technology has led to the need for a secure and cost-effective means of linking funders with developers. With a wide range of technological directions available, it is important to establish an efficient and trustworthy crowdfunding platform. Blockchain technology offers a decentralized network of users, where transactions are recorded in a distributed ledger, providing transparency and cost-effectiveness for various applications. In light of this, we propose a global crowdfunding platform, utilizing the features of blockchain technology to connect funders and developers as different nodes of the network. A smart contract is deployed between them to arrive at an optimal solution for the funders, with the process repeated between developers until reaching an equilibrium. Our experimental results indicate that this model outperforms generic crowdfunding algorithms.

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Blockchain, Crowdfunding, Ethereum, Smart contract

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Creative Commons Attribution 4.0 and The Open Definition

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The paper introduces a novel framework called Nonlocal Means based Framework (NMF) for image denoising. The goal is to reduce noise in digital images without compromising important image features. While existing methods often focus on removing either additive white Gaussian noise (AWGN) or impulse noise (IN) separately, this framework addresses the challenge of images corrupted by a mixture of both types. The proposed framework combines the strengths of the median filter and nonlocal means to tackle the noise mixture. The median filter, a non-linear filtering technique, is employed to identify outlier pixels in the image, which are then replaced by nonlocal means. This step effectively separates the mixed noise into its Gaussian components. To further enhance denoising performance, the framework utilizes a low-rank approximation combined with NMF (LRNM) model. This model groups similar nonlocal patches into a matrix and applies a low-rank approximation to reconstruct the clean image. Additionally, a Convolutional Neural Network (CNN) is integrated with NMF (NMF-CNN) to demonstrate the versatility of the NMF approach. The experimental results highlight the effectiveness of LRNM and NMF-CNN in removing mixed noise and producing visually pleasing denoised images. These approaches demonstrate strong performance in noise reduction and can contribute to improving the quality of images captured by modern cameras.

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 Keywords

Non Local Mean, CNN, Mean squared error (MSE), Structural similarity index (SSIM), Gaussian Noise, Impulse Noise.

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT Ahara (food) is referenced in Ayurvedic texts as one of the three Upasthambas (Sub-pillars of the Body) that support the three main Sthambas (Pillars) of the Body. Ahara is considered necessary for the human body since it offers the basic nutrients required to carry out the basic operations of digestion and metabolism. Ayurveda emphasizes the importance of eating a balanced and nutritious diet. A physically balanced diet can frequently disrupt equilibrium. Food, when consumed in the appropriate manner, nourishes the person both physically and psychologically, and it is through food that a person achieves positive health and bodily growth. Food consumed in an unbalanced manner can induce a variety of diseases. Ayurveda clearly defines that certain diet and its combinations, which interrupts the metabolism of tissue, which inhibits the process of formation of tissue and which have the opposite property to the tissue are called as Viruddha Anna or incompatible diet. The food which is wrong in combination, which has undergone wrong processing, which is consumed in incorrect dose, which is consumed in incorrect time of day and in wrong season can lead to Viruddha Ahara.

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Key word- Ayurveda, Virudh Aahar, Incompatible Foods

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Realty sector in India has attracted the prospective investors to commit their money in the concrete world of real estate with an anticipation to multiply the lucre than never before. This led various biggies like Omaxe, Indiabulls , Unitech and many more to emerge as market players in the realty sector too. Emergence of various realty players in the market baffles the investors as to where to invest to not just keep their hard earned money safe but also to ensure good returns. So, it is important to know how efficiently a realty company is actually working. The aim of this paper is to measure the relative efficiencies of the 40 major listed companies in realty sector using the non-parametric technique Data envelopment analysis , which is a data oriented approach to evaluate the performance of a set of peer entities. Based on the annual report for the year 2012 ,of these companies, various financial ratios have been recognized as inputs and as outputs to evaluate the efficiencies.

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 Keywords

Keywords: Performance measurement, Financial Variables, Data Envelopment Analysis

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Creative Commons Attribution 4.0 and The Open Definition