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Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. 2024 Author(s). -
ML based sign language recognition system
This paper reviews different steps in an automated sign language recognition (SLR) system. Developing a system that can read and interpret a sign must be trained using a large dataset and the best algorithm. As a basic SLR system, an isolated recognition model is developed. The model is based on vision-based isolated hand gesture detection and recognition. Assessment of ML-based SLR model was conducted with the help of 4 candidates under a controlled environment. The model made use of a convex hull for feature extraction and KNN for classification. The model yielded 65% accuracy. 2021 IEEE. -
ML-Based Prediction Model for Cardiovascular Disease
In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Mobile Freeze-Net with Attention-based Loss Function for Covid-19 Detection from an Imbalanced CXR Dataset
In this paper, we present a novel framework, that is, Mobile Freeze-Net along with Attention-based Loss Function, for Covid-19 detection from a Chest X-Ray (CXR) dataset. First, we have observed that by freezing 50% of a Mobile Net-V2 model (means fine-tuning 50% layers from ImageNet dataset) has automatically removed the class imbalance problem from the CXR dataset considerably. We call this 50% frozen Mobile Net-V2 model as Mobile Freeze-Net. Secondly, we have proposed an Attention-based Loss function, which provides more attention to the class, having higher inter-class similarity. We have computed attention weights for each class from the statistical inference of the dataset itself, by employing a Monte-Carlo method and thereafter, we have incorporated those weights into WCCE loss function of Mobile Freeze-Net model. By utilizing Mobile freeze-Net, we have achieved testing accuracy, F1 score, precision and recall of 93%, 94%, 93% and 94% respectively. This is approximately 3-4% improvement compared to 100% fine tuning of Mobile-Net V2. Furthermore, we have achieved approximate 1-2% improvement of Mobile Freeze-Net, after incorporating Attention-based Loss function. For the validity of the proposed framework, we have conducted experiments with 10-fold cross validation. All these experimental results suggest that our proposed framework has outperformed other existing models considerably. 2023 Owner/Author(s). -
Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization
With the rapid improvement of market economy and modern logistics technique, the logistics distribution link is receiving more and more attention, and the logistics distribution path question in distribution has become the core question in logistics distribution. Study the optimization of logistics distribution path. Logistics distribution path optimization needs to find an optimal distribution route with less distribution vehicles and the shortest total length of the path, and has the rapidity of distribution. The traditional algorithm takes a long time to search the optimal route, which makes it difficult to find the optimal distribution route, resulting in high logistics distribution costs. In order to quickly find the optimal distribution route and improve the quality of logistics service, a logistics model based on particle swarm optimization algorithm is proposed. The group is composed of several non-intelligent individuals or groups of individuals. Each individual's behavior follows certain simple rules and has no intelligence; Individuals or groups of individuals can cooperate to solve questions through certain principles of message exchange, thus showing the behavioral characteristics of collective intelligence. After research, the algorithm in this paper is effective and suitable for wide application in practice. 2023 IEEE. -
Model independent approach to photodisintegration of 7Li at the range of energies of interest to BBN
One of the elements that was synthesized primordially in the standard Big Bang Nucleosynthesis is lithium. Lithium, being fragile gets easily destroyed at relatively low temperatures in the mixing process between stellar surface and hot internal layers. So that, at the end of the stellar lifetime the lithium content is believed to be depleted. Series of experimental measurements on lithium isotopes were carried out at High Intensity Gamma Ray Source (HIGS) at Duke Free Electron Laser Laboratory. More recently experiments [1]-[2] were performed, to measure the differential cross section of the photo-neutron reaction channel in photodisintegration of 7Li, where the progeny nuclei is in the ground state as well as in excited states. The purpose of present contribution is to study the reaction channel 7Li + ? ? 6Li + n using linearly polarized photons.The model independent irreducible tensor formalism [3]-[5] will be used to study the differential cross section of the reaction. We study the angular dependence of differential cross section by expressing differential cross section in terms of legendre polynomials. In view of the several theoretical and ongoing experimental studies, a detailed theoretical study of the spin structure of the amplitudes in 7Li+ ? ? 6Li+ n and their expansion in terms of'electric' and 'magnetic' amplitudes is needed to analyze the measurements of spin observables as well as differential cross section, which leads to a better understanding of the problem at astrophysical energies. 2022 Institute of Physics Publishing. All rights reserved. -
Model independent approach to proton polarization in photodisintegration of deuteron
In addition to other photonuclear reactions, the study of photonuclear reactions on deuterium targets is important for laser physics, nuclear physics, astrophysics, and a number of applications, including nondestructive testing of nuclear materials. In this paper, we have carried out a model independent analysis of proton polarization in photodisintegration of deuterons with initially unpolarized beam and unpolarized target. The angular dependence of the polarization is studied by expressing it in terms of multipole amplitudes. 2023 Elsevier Ltd. All rights reserved. -
Model Selection Procedure in Alleviating Drawbacks of the Electronic Whiteboard
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 IEEE. -
Modeling a Logistic Regression based Sustained Approach for Cancer Detection
This assessment and treatment of cancer may be done using logistic regression. To properly forecast whether a tumour is malignant or benign, the likelihood of binary outcomes may be simulated based on input variables and taken into account for factors like volume, topology and texture. It aids in risk assessment by estimating an individual's likelihood of developing cancer using factors like age-group, relatives past data, life choices and gene based markers. Logistic regression plays an important role in early cancer detection and creating screening tools that identify high-risk individuals through patent characteristics, biomarkers, and medical imaging data. Prediction of the probability of survival based on age, tumor characteristics, treatment options and comorbidities is useful for survival analysis. In a comparative study, logistic regression achieved a high accuracy of 97.4%, along with random forest, in cancer detection and diagnosis. 2023 IEEE. -
Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
Modelling Networks withAttached Storage Using Perfect Italian Domination
Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach
Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. -
Moderation of Income and Age on Customer Purchase Intention of Green Cosmetics in Bangalore
Cracking the code of customer purchase behaviour is a challenge for market researchers as myriad factors interfere. Marketers are puzzled as competitors position a new product category in the market to create demand. Indian public perceived cosmetics composition as blend of healthy chemical extracts. Television commercials portrayed the presence of chemicals in cosmetics as a product performance booster. People attributed chemical presence to superior product performance. Saturated markets witnessed competitors aiming at increased sales with similar commercials. Under pressure to differentiate, the idea of organic cosmetics started. Companies invested heavily on product development, marketing and branding. Expected success was not achieved as buyers measured performance of cosmetics weighing the absence of chemicals. Scepticism on organic level of the products emerged as various brand commercials claimed their respective compositions a true organic product. Fewer studies explained purchase intention of green cosmetics without focus on health consciousness and consumer innovativeness. Product diffusions were strategized on the basis of consumer innovativeness. Health consciousness captured individuals weightage on health and well-being while purchasing a product. This paper explores relationship of health consciousness and consumer innovativeness with purchase intention development conducting exploratory factor analysis, regression analysis and interaction analysis on selected independent variables using dependent variables. The study found both consumer innovativeness and health consciousness leading to development of purchase intention of green cosmetics. Age and income moderated the relationship of consumer innovativeness and purchase intention. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Modern Technology Usage for Education Field during COVID-19: Statistical Analysis
The COVID-19 pandemic has had vast effects on the concept of education as a whole. During the pandemic, students had no access to physical teaching practices, which had been adapted worldwide as the principal way of education since the 1800's. Due to the restrictions imposed to garner safety from the spread of the virus, this methodology had to be modified based on the situation at hand. Alternatives through the usage of Virtual Learning Platforms (VLP), Online Tutoring Platforms (OTP), Web Conferencing Platforms (WCP) and multiple assessment tools like plagiarism checker, poll sites, quiz platforms, online proctored examinations (OPE) started gaining popularity among all institutes to cope with the limitations levied. The technologies molded a path for student-teacher interaction, performance assessments, document sharing and online tutoring. This research highlights the lack of online tutoring equipment, educators' limited expertise with online learning, the knowledge gap, a inimical atmosphere for independent study, equity, and academic success in postsecondary learning. The goal of this review is to present an overview of available technologies for online teaching that can be used to improve the quality of education during COVID-19. 2022 IEEE. -
Modernized energy management system: A review
The usage of renewable energy system (RES) and its management is vital for reliable electrical energy delivery without pollution. In the scenario of increase in distributed generations (DGs), to utilize the generated electricity from RES without any wastage, to avoid the consumption of electricity during peak hours, to store and retrieve energy in an efficient way from the battery, there is a need for overall energy management system (EMS). As the prices for electricity and pollution are reduced, the review of available methodologies is discussed in this paper. The EMS takes decision based on the predicted load demand. So, the different prediction methodologies and their benefits are also discussed here. Though the electric vehicles (EVs) are considered as load in power system, the storage facility of the EVs are also used as power backup facilities through vehicle to grid (V2G) technology. This paper provides a review on the complete management of RES, EVs, batteries and load. Published under licence by IOP Publishing Ltd. -
Modernizing Electrical Grids with TCR-Based Flexible AC Transmission Systems
Modernizing electrical grids is imperative to meet the growing demand for reliable, efficient, and sustainable energy. Thyristor-Controlled Reactors (TCRs) are integral components of modern Flexible AC Transmission Systems (FACTS). These systems offer a robust solution for enhancing grid stability, improving power quality, and optimizing transmission efficiency, ensuring that electric grids can support future energy needs. TCR-based FACTS are a collection of technologies designed to enhance the controllability, stability, and power transfer capability of AC electrical grid systems. In this paper, we will discuss the role of TCRs in modernizing AC transmission systems and their role in addressing grid challenges and improving performance, highlighting their critical role in future grid infrastructure. To discuss the future prospects and developments in TCR technology, with ongoing advancements and research efforts paving the way for more efficient, reliable, and flexible grid management solutions. The Authors, published by EDP Sciences, 2024. -
Molecular simulations to investigate the guest-induced flexibility of Pu-UiO-66 MOF
Actinide metal-organic frameworks are highly popular because of their significant coordination benefits. Due to production and characterisation challenges, An-MOFs are a relatively less explored coordination polymer. In this study, we considered the experimentally synthesised Pu-UiO-66 MOF, which was the first reported plutonium MOF. In most MOF studies, the framework has been maintained rigid, however, in this case, we investigate both rigid and flexible frameworks. To gain a better understanding of the framework's flexibility, flexible Grand Canonical Monte Carlo (GCMC) simulations were conducted and the calculated results were compared with that of rigid frameworks. Molecular Dynamics (MD) simulations were carried out to examine the effects of framework flexibility of Pu-UiO-66 MOF, a force field-built Grand Canonical Monte Carlo (GCMC) on adsorption of guest molecules, and to analyse the self-diffusion coefficients of acidic gases such as CO2, SO2, and NO2 in the framework. The adsorption isotherms and radial distribution functions for both rigid and flexible frameworks in the presence of gas molecules were compared and analysed using GCMC simulation. Similarly, molecular dynamics simulations including guest molecules were carried out. Following that, the GCMC and MD results were compared and analysed to determine the flexibility of the system. Diffusion studies were conducted at various temperatures and the coefficient of self-diffusion of each gas was examined. In addition, structural analyses, such as angle analysis, were carried out to explore the local changes, such as tilting, observed in the organic ligand derivative. It was also shown that the UFF force field is suitable for Pu-UiO-66. 2022 -
Monitoring and Controlling Data Through the Internet of Things (IOT) System: A Framework to Measure the Public Health
Associating and sharing information by means of the web between actual things, or 'things,' coordinated with sensors, programming, and different advances are known as the Internet of Things (IoT). In order to improve technology through IoT, there have been a number of important studies and investigations. This study exhibits how the Internet of Things might be utilized to screen wellbeing. In this research work, with the help of IoT based human wellbeing checking framework the information circulatory strain, beat rate, internal heat level, pulse, and other crucial signs are providing to the internet. The use of IoT for the human health monitoring system in later on future, need a very accurate assessment of risk and this is required to provide a long term information to the device. 2022 IEEE. -
Monitoring nyiragongo volcano using a federated cloud-based wireless sensor network
Current Nyiragongo Volcano observatory systems yield poor monitoring quality due to unpredictable dynamics of volcanic activities and limited sensing capability of existing sensors (seismometers, acoustic microphones, GPS, tilt-meter, optical thermal, and gas flux). The sensor node has limited processing capacity and memory. So if some tasks from the sensor nodes can be uploaded to the server of cloud computing then the battery life of the sensor nodes can be extended. The cloud computing can be used both for processing of aggregate query and storage of data. The two principal merits of this paper are the clear demonstration that the Cloud Computing model is a good fit with the dynamic computational requirements of Nyiragongo volcano monitoring and the novel optimization algorithm for seismic data routing. The proposed new model has been evaluated using Arduino-Atmega328 as hardware platform, Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. 2017 IEEE.