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Blockchain Enabled Model for Minimizing Post Harvest Losses
Post-harvest loss (PHL) leads to both decline in quantity and quality of food processing output from harvest to consumption. They can be caused by a wide range of circumstances, from growth conditions to retail handling. As storage loss is considered one among the detrimental factors, in this study, 25 data units were collected from a cold storage facility to analyze and focus specifically on post-harvest losses of vegetables. Various data analysis was carried out using SPSS tool. It was found that majority of losses were due to pest infection, weight losses due to climatic conditions, and transportation losses. On the other hand, block chain being a trend setter in the recent technology evolution which is providing fruitful outcomes in all the integrated fields, we have chosen the same for obtaining a better solution for the afore mentioned problem. Integrating blockchain technology into the structure can significantly reduce storage losses and support producer-consumer lines. The Electrochemical Society -
Data Analysis and Machine Learning Observation on Production Losses in the Food Processing Industry
Food wastage and capturing lineage from production to consumption is a bigger concern. Yielding, storage and transportation areas have evolved to a great extent associated to manufacturing and automation which lead to technical advancements in food processing industry. In such situation, losses are generally observed in the crop production which are sometimes minimal and ignored. However, in some cases these losses are huge and are becoming a threat to the both producers and consumers. Here we considered data related to dairy products and analysed the production losses especially while processing them in the treating unit. Literature on parameters and associated data analysis in the form of graphical representation are provided in the appropriate sections of the paper. Linear regression and correlation were envisaged in view of incorporating machine learning techniques understanding production losses. Karl Pearson's correlation provides an observation related to association of parameters which are desired to be less coupled in terms of employing proposed newer methodology. 2023 IEEE. -
A real time fog computing applications their privacy issues and solutions
Edge Computing (EC) has brought cloud technology to the channel's edge. It inherits some qualities from cloud services, but it also has some distinctive features such as geo-distribution, network connectivity, and reduced power. Along with the genetic inheritance, it also acquires the issues and concerns cloud computing services, such as renewable energy and resource allocation. This work provides a critical analysis of the fog architectural design in terms of security. Since 2018, the state of the artwork has been critically analyzed in terms of security mechanisms and security threats. The existing security methods are classified based on the security objectives they achieved. It would provide a complete and coherent difference between both the security areas investigated and those that have not. 2021 IEEE. -
Low-Velocity Impact Characteristics of GLARE Laminates with Different Sheet Thickness
Fiber reinforcement with metallic face sheets is one of the recently implemented advanced materials in distinctive applications such as fender, bonnet, and chassis used in automotive sectors. While the reinforcement enhances the sustenance property of the laminate, the face sheets provide resistance to impact force. In most automotive sectors, drop-weight analysis at varying velocity range is performed to evaluate the damage characteristics of the vehicle body. The present work is aimed at studying the influence of low-velocity impact (LVI) on glass laminate aluminum-reinforced epoxy (GLARE) laminate. Three distinct thicknesses of Al-2024 T3 aluminum alloy (0.2, 0.3, and 0.4 mm) were chosen as the face sheet, the overall thickness was kept at 2.0 mm for all the cases. Absorbed energy and damage characteristics of GLARE for different energy was experimentally determined using drop-weight impact tester. From the results, it was found that GLARE laminate can sustain a maximum impact energy of around 20 J, beyond which damage in the form of cracks begin to occur at bottom face sheet also. It was also evident that laminate can sustain impact at a velocity of 3.13 m/s and beyond which it leads to delamination damage at 3.49 m/s. Further, it is noticed that GLARE laminate with 0.3 mm face sheet thickness has best results with reference to both absorbed energy and damage when compared with other thicknesses. Also, the sample B indicates the optimal surface texture when subjected to LVI damage obtained through scanning electron microscope (SEM). 2021 SAE International. -
Blockchain Integrated Retail Logistics Chain: An Adoption Perspective
The task of managing public health and safety is a multifaceted and delicate one that demands the careful upkeep of numerous processes and systems, with a particular emphasis on cold chain logistics. The primary objective of this research is to investigate how blockchain technology can meet the needs of a retail cold chain. To accomplish this goal, we employed a comprehensive technological adoption model, partial least squares structural equation modeling, and a quantitative cross-sectional survey approach to ascertain stakeholder adoption intentions toward a blockchain-enabled cold supply chain. Our findings suggest that blockchain technology has the capacity to effectively facilitate the goals of the retail cold chain. 2024 IEEE. -
Characteristic Mode Analysis of Fashion Brands Conductive Logos as Potential Radiators
A few popular fashion brand logos, which can be employed as potential radiating elements, are investigated in this paper based on the theory of characteristic mode (TCM). Such an analysis would further help design multi-band wearable antennas within the frequency range from 1 to 6 GHz. The resonant behavior and bandwidth capability for various modes are presented and discussed. It is observed that all the studied logos demonstrate a first resonant frequency around 1.5 GHz, while both Lacoste and Louis Vuitton logos show wider modal bandwidths capabilities. 2023 IEEE. -
Blockchain Integrated Pharmaceutical Cold Chain: An Adoption Perspective
A complex and sensitive chain needs to be appropriately maintained to manage public health and people's lives. This is especially true of the cold pharmaceutical chain. The primary objective of this study is to explain how blockchain adaption might meet a pharmaceutical cold chain's requirements. A comprehensive technological adoption model, partial least square structural equation modeling, and a quantitative cross-sectional survey approach were utilized to identify stakeholder adoption intentions toward a blockchain-enabled cold supply chain. This study provides evidence that blockchain technology has the potential to support the objectives of the cold pharmaceutical chain. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Environmental Concern in TPB Model for Sustainable IT Adoption
Rapid advancement in technology and continuous environmental degradation has attracted the attention of practitioners toward sustainable solutions. This study aims to investigate educated millennial beliefs and behavior toward sustainable IT practices. The Theory of Planned Behavior (TPB) model deployed in the study was extended through perceived environmental responsibility. A survey was conducted to examine the sustainable IT adoption behavior of millennial in the National Capital Region, Delhi India. Variance based partial least square structure equation modeling was employed to evaluate the hypothesized model. Findings of the study confirm environmental concern (ER) a precursor for attitude (ATT), perceived behavioral control (PBC), and subjective norm (SN). Further, there is a significant positive influence of ATT, PBC, and SN on the adoption intention of sustainable IT practices, followed by the effect of adoption intention on actual adoption behavior. Study disseminates valuable insights to policymakers and marketers to formulate strategies and policies to attain sustainability through sustainable IT practices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Characteristic Mode Analysis of Metallic Automobile Logo Geometry
This paper presents a characteristic mode analysis of a few popular automobile logo geometries. It is performed to get an insight into the physical behavior of those geometries which can be employed as a radiating element, such as an antenna. Such an analysis helps design multi-band and multi-mode antennas suitable for 5G sub-6 GHz bands. The resonant behavior, bandwidth capability, and modal current distribution analysis are presented for various modes of different automobile logo geometries, demonstrating that Audi, Suzuki, and Volkswagen logos show multi-band performance. Moreover, due to having symmetric modes, the BMW logo was found to be suitable for designing a circularly polarized antenna. 2023 IEEE. -
Characteristic Mode Analysis of Closed Metal Geometric Ring Shapes
In this study, the characteristic mode theory is used to better explain the physical behavior of a few simple closedshaped geometries. The bandwidth coverage, resonant behavior, and modal current distributions for several ringshaped geometries are shown and discussed. It has been demonstrated that the triangular, rectangular, and square ring geometries can result in multi-band performance, whereas the hexagonal, circular, square, and triangular rings are promising candidates for circularly polarized antenna designs. 2024 IEEE. -
Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry
Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. 2024 IEEE. -
Machine Learning Observation on the Prediction of Diabetes Mellitus Disease
Diabetes disease has become as one of the common syndromes in many of the age groups. Diabetes can result in high blood sugar levels, a heart attack, or heart disease. This is one of the fastest developing illnesses, and it requires regular care. After seeing the doctor and being diagnosed, the patient is typically compelled to obtain their reports. Because this procedure is time-consuming and costly, we have the option of using ML approaches to solve this problem. Our research aims to foster a framework prepared to do all the more precisely foreseeing a patient's diabetes risk level. To develop models, classification methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest Classifier are employed. The results indicate that the techniques are quite accurate. The result showed that the prediction with the Logistic Regression model acquired the highest accuracy. 2023 IEEE. -
Employee Attrition, Job Involvement, and Work Life Balance Prediction Using Machine Learning Classifier Models
Employee performance is an integral part organizational success, for which Talent management is highly required, and the motivating factors of employee depend on employee performance. Certain variables have been observed as outliers, but none of those variables were operated or predicted. This paper aims at creating predictive models for the employee attrition by using classifier models for attrition rate, Job Involvement, and Work Life Balance. Job Involvement is specifically linked to the employee intentions to turn around that is minimal turnover rate. So, getting justifiable solution, this paper states the novel and accurate classification models. The Ridge Classifier model is the first one it has been used to classify IBM employee attrition, and it gave an accuracy of 92.7%. Random Forest had the highest accuracy for predicting Job Involvement, with accuracy rate of 62.3%. Similarly, Logistic Regression has been the model selected to predict Work Life Balance, and it has a 64.8% accuracy rate, making it an acceptable classification model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Microstructure and Mechanical Behaviour of Al6061-ZrB2 In-situ Metal Matrix Composites
Aluminium matrix composites processed through in-situ molten reaction has emerged as an alternative for eliminating defects existing in ex-situ reinforced metal matrix composites. Development of composites through in-situ method using inorganic salts via liquid metallurgy route is the most widely accepted technique. In the present work, Al6061-ZrB2 in-situ composites have been developed through in-situ reaction of Al-10%Zr and Al-3%B master alloys in Al6061 alloy. Study of microstructure and mechanical properties of in-situ reinforced ZrB2 in Al6061 alloy have been carried out. Composite exhibited grain refinement and improved the mechanical properties of Al6061 alloy. Ductility of composite is reduced with increase in content of ZrB2. Published under licence by IOP Publishing Ltd. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
Three-component p-TSA catalyzed synthesis of hydrazinyl thiazole derivatives
A direct single-pot three-component procedure for synthesizing bio-active hydrazinyl thiazole derivatives has been demonstrated. The reaction involves substituted 2-Bromoacetophenones, carboxaldehydes, and thiosemicarbazide to form the hydrazinyl thiazole scaffolds via a simple condensation reaction followed by intramolecular cyclization with p-TSA as a catalyst at room temperature. The ease of product separation, lack of column chromatographic purification, and use of readily available starting materials result in an efficient approach for organic synthesis. 2023 Elsevier Ltd. All rights reserved. -
An Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks
In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP
Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. 2024 IEEE.