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Arming Farmers with Smart Farming: The Future of Agriculture
Internet of Things (IoT) innovation is currently one of the growing fields across a diversity of industries, together with agriculture. IoT enhances our lives by making and promoting developments in a wide range of actions to encourage them to become more appropriate, practicality, and enhanced using suitable man-made recognition. Smart agricultural frameworks recognize a social trade toward more helpful, lower-cost agribusiness because of this innovation. The proposed work is to use IoT in the agriculture industry to collect real-time data (soil moisture, temperature, and so on) to help one look at a few climate scenarios from afar, efficiently, and greatly increase production. A global solution for monitoring and managing the agricultural field remotely has been proposed. Implementation of a local stand-alone field control unit that includes detection and activation capabilities. Developed a cloud solution for data storage, real-time monitoring, and historical data visualization based on the ThingSpeak cloud platform. Remote managing and control functions have been realized in both the local unit and the cloud using IoT infrastructure. 2022 IEEE. -
Novel super stack passivation in AlGaN/GaN HEMT for power electronic applications
A super-stack passivation technique is proposed for an AlGaN/GaN HEMT in order to improve the breakdown voltage and cutoff frequency. The performance of the proposed technique is benchmarked against a conventional GaN HEMT. The analysis and investigation are carried out using Technology Computer-Aided Design (TCAD). The simulation results are validated with experimental data. It is observed that the breakdown voltage of the conventional and proposed devices is 356V and 449V, respectively. In contrast to the conventional device, the breakdown voltage of the proposed device is improved by 21%. This is the manifestation of the suppression of the electric field by the super-stack passivation technique in the proposed device. Furthermore, it is also observed that the Johnsons figure of merit in the proposed GaN-HEMT is also improved. 2024 The Author(s). Published by IOP Publishing Ltd. -
An Analytical Review on Data Privacy and Anonymity in 'Internet of Things (IoT) Enabled Services'
Nowadays, the Internet of Things (IoT) is an emerging technology, spreading all over the world so the number of devices is increasing day by day. So, the volume and data complexity has increased drastically in the past three years. The resultant system might contain a significant number of heterogeneous devices, posing integration and scalability issues that must be addressed. In such a situation, security and privacy are commonly regarded as a significant concern. On the other hand, user privacy, defined as the capacity to provide data protection and anonymity, must be protected, which is especially important when personal and/or sensitive information is involved. This paper presents the comprehensive survey, characteristics, and application of IoT and the immense number of challenges raced and faced during the implementation of IoT frameworks. 2021 IEEE. -
Cross Correlation Between Plasmaspheric Hiss Waves and Enhanced Radiation Levels at Aviation Altitudes
Enhanced radiation in the Earth's atmosphere can pose serious hazards to pilots, aircraft passengers, and commercial space travelers. Recent results have shown, statistically, that there is a strong correlation between dose rates observed by Automated Radiation Measurements for Aerospace Safety (ARMAS) instruments at aviation altitudes (>9km) and plasmaspheric hiss wave power measured by NASA's Van Allen Probes within the inner magnetosphere. Plasmaspheric hiss waves play a very important role in removing energetic electrons from the Earth's radiation belts by precipitating them into the upper atmosphere. These relativistic electrons generally drift eastwards along closed magnetic drift shells. In this study, we use magnetic conjunction events between ARMAS and the Van Allen Probes to analyze the causality between plasmaspheric hiss waves and enhanced radiation observed at aviation altitude. We specifically study how the size of the conjunction window and a shift in L and MLT of the conjunction window affect the correlation between dose rates and plasmaspheric hiss wave power. This is to determine if the observed enhanced radiation at aviation altitude is indeed caused by the plasmaspheric hiss waves in the inner magnetosphere. The results show that the enhanced radiation levels are only correlated with plasmaspheric hiss waves within conjunction windows of ?1 (Formula presented.) L (Formula presented.) 1 and 0 (Formula presented.) MLT (Formula presented.) 2. The correlation between dose rate and hiss wave power increases slightly if ARMAS is shifted approximately 1hr in MLT to the east of the Van Allen Probes, consistent with the drift trajectory of the electrons precipitating into the atmosphere. 2025. The Author(s). -
Experimenting with scalability of floodlight controller in software defined networks
Software Defined Network is the booming area of research in the domain of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of existing multiple controllers providing the SDN functionalities to the network, one of the stellar controllers is Floodlight Controller. This paper is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Floodlight Controller and iPerf. Floodlight Controller is tested in the simulation environment by observing throughput and latency parameters of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes. 2017 IEEE. -
Scalability of software defined network on floodlight controller using OFNet
Software Defined Network is the thriving area of research in the realm of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the competence and applicability to hold up the fault tolerance of the solution offered in the form of SDN Controllers. Out of the accessible multiple controllers with enabled the SDN functionalities to the network infrastructure, one of the best choice in controllers is Floodlight Controller. This research article is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing dual scenarios implemented, experimented and analyzed on the emulation tool of OFNet. Floodlight Controller is tested in the emulation environment by observing eight different parameters of the controller and checked its performance in scalable networking conditions over linear topology by gradually increasing the number of nodes. 2017 IEEE. -
Ryu controller's scalability experiment on software defined networks
Software defined networks is the future of Computer networks which claims that traditional networks are getting replaced by SDN. Considering the number of nodes everyday connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution as SDN to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of the existing multiple controllers providing the SDN functionalities to the network, one of the basic controllers is Ryu Controller. This paper is a contribution towards performance evaluation of scalability of the Ryu Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Ryu Controller and iPerf. Ryu Controller is tested in the simulation environment by observing throughput of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes until it supported tested on high end devices. 2018 IEEE. -
School alienation in online schooling scale (SAOSS): development of a measure to assess school alienation among students
This study reports on developing and validating the School Alienation in Online Schooling Scale (SAOSS). School alienation in online schooling is conceptualized as opposed to school belonging and the main reason for minimal participation, reduced educational benefits, and school dropout in school aged-children, especially during the online learning forced due to the COVID-19 Pandemic. This study includes three phases. The first study included item generation and analysis. After the initial analysis, 13 items from the tool were retained. In the second study, exploratory factor analysis was conducted. Two factors emerged from principal component analysis (PCA). In the third study, we investigated the confirmatory factor analysis of SAOSS with a sample of urban students in Iran (grades 79, n = 317). The SAOSS has important implications for researchers, school counselors and psychologists, policymakers, and stakeholders. Implication for theory, practice and future research is discussed. 2024 International School Psychology Association. -
Student Subjective Wellbeing amidst the Covid-19 Pandemic in Iran: Role of Loneliness, Resilience and Parental Involvement
The COVID-19 pandemic and lockdowns potentially severely impact adolescents mental well-being. This research aims to study students subjective well-being during the covid-19 pandemic in Iran and investigate the role of loneliness, resilience, and parental involvement. For this study, 629 students (female = 345) were recruited by purposive sampling. Students were assessed on the Students Subjective Well-Being, Loneliness Scale, Resilience Scale, and Parental Involvement. The results confirm our hypothesis that the relationship between parental involvement and students subjective well-being is mediated by loneliness. Furthermore, the results indicated a partial mediation of resilience in the relationship between parental involvement and students subjective well-being. This study theoretically contributes to a better understanding of the factors determining the impact of traumatic events such as a pandemic on adolescents mental health. The implications of this study indicate interventions that can be carried out to minimize the negative psychological consequences of the pandemic. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
A Novel Technique for Magnetic Particle Separation Using Current-Carrying Slotted Plate
In this paper, a novel method for separating and trapping different magnetic particles is presented. Changes in the current-carrying structure yield disturbing the generated magnetic field. Here, slots were innovatively crafted on the current-carrying plate positioned beneath the microchannel, resulting in a non-uniform magnetic field distribution. This breakthrough enables the separation of different particle types using a constant and low electric current for the very first time, leading to a significant advancement in the field. More importantly, this proposed technique offers several advantages, including the generation of low levels of current and heat, ease of construction, and the ability to control the magnetic field produced by the electric current. In this study, the capability to effectively separate various particle types using a constant electric current was demonstrated with a remarkable separation efficiency of about 100%. By applying a 100[mA] electric current to the plate that carries electric current, the separation of two particle types M-450 and M-280 was achieved at a velocity of 2[?m/s]. 2024 IEEE. -
One Time Password-Based Two Channel Authentication MechanismUsing Blockchain
Using Fog Nodes, also known as IOT devices are increasing everyday with more and more home automation, industry automation, automobile automation, etc. Security threats for these devices are also increasing. One of the threats is impersonating one fog node, stealing data and taking control of the network which is also known as the Sybil attack. To provide security, most fog devices use one step or two step authentication and sometimes use encryption. With static passwords, there is a chance of compromise by password sharing and leaking. Some weak encryption algorithms used are also compromised. Data about fog nodes in the network is stored in a weak database and is tampered. OTP-based Two Channel Authentication Mechanism (OTPTAM) to authenticate the fog nodes with metadata stored in Blockchain Database and communicate using channels encrypted with Elliptical Ciphers can solve the majority of these problems. Metadata of the nodes like Bluetooth MAC address, network mac address, telephone number are all stored in the blockchain and the OTP is exchanged via these channels to ensure the authenticity of the fog nodes. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture
The paper explores the application of Convolutional Neural Networks (CNNs), specifically ResNet-18, in revolutionizing the identification of diseases in tomato crops. Facing threats from pathogens like Phytophthora infestans, timely disease detection is crucial for mitigating economic losses and ensuring food security. Traditionally, manual inspection and labour-intensive tests posed limitations, prompting a shift to CNNs for more efficient solutions. The study uses a well-organized dataset, employing data preprocessing techniques and ResNet-18 architecture. The model achieves remarkable results, with a 91% F1 score, indicating its proficiency in distinguishing healthy and unhealthy tomato leaves. Metrics such as accuracy, sensitivity, specificity, and a high AUC score on the ROC curve underscore the model's exceptional performance. The significance of this work lies in its practical applications for early disease detection in agriculture. The ResNet-18 model, with its high precision and specificity, presents a powerful tool for crop management, contributing to sustainable agriculture and global food security. (2024), (Science and Information Organization). All Rights Reserved. -
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE. -
India's Consumer Protection Bill 2015: Redefining notions of liability
The legislative validation of the concepts of product liability and unfair contractual terms through the Consumer Protection Bill (CPB) paves the way for attuning Indian consumer laws with the global wavelength. Though the Indian judiciary has dealt with issues concerning product liability and unfair contractual terms, legal certainty has remained elusive, necessitating predictability. The extension of consumer protection to the domain of e-commerce further reformulates the existing notion of liability and the prevalent impunity. This paper analyses the manner in which the CPB redefines the notions of liability and the existent lacunae that need to be remedied. The Author 2017. Published by Oxford University Press. All rights reserved. -
Exact solution of non-coaxial rotating and non-linear convective flow of cual2 o3 h2 o hybrid nanofluids over an infinite vertical plate subjected to heat source and radiative heat
This paper investigates the non-linear convective flow due to non-coaxial rotation of vertical planar plate by utilizing three different liquids namely H2 O (water), Al2 O3 H2 O (nanofluid) and CuAl2 O3 H2 O (hybrid nanofluid). The impacts of Rosselands radiative heat and internal heat generation are also included in this study. The non-coaxial rotation of the plate crafts sine or cosine oscillations in its plane and the liquid at infinity. The density-temperature relation is studied which is nonlinear and causes a nonlinear convective heat transfer. The dynamic viscosity, thermal conductivity, density and specific heat of hybrid-nanofluids are assumed to vary with the volume fraction. These thermophysical properties of hybrid and nanofluids are determined by phenomenological laws and mixture theory. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The LTM (Laplace Transform Technique) is employed to find the exact solutions. The flow, skin friction and thermal features are scrutinized with the discrepancy of governing parameters. The effective fluid properties and the Nusselt number are also calculated for sixteen different hybrid-nano-liquids. 2019 by American Scientific Publishers All rights reserved. -
Design and Implementation of Smart Manufacturing Systems Through AR for Data-Driven Digital Twin System
Modification of size, residual stress, and surface roughness have an enormous impact on a complex mechanical products final machining quality. Machine quality can be ensured using Digital Twin (DT) technology by checking the real-time machining process. The virtualreal separation display method is the most modern DT System (DTS). It results in the ineffective transmission of the necessary restricting the use of the DTS by processing data on-site technicians to support field processing. Augmented Reality (AR) monitoring the manufacturing process approach to solve this problem is proposed based on the DT. First, the dynamic multi-view for AR is built using data from multiple sources. Second, real-time monitoring of complex products intermediate processes incorporates AR to encourage communication between the users of the DT machining system. The outcome of the system can prevent errors that cannot be fixed. An application case for observing will be used to confirm the viability and the efficacy of the proposed method. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
ML Algorithms and Their Approach on COVID-19 Data Analysis
This chapter begins with characterizing Supervised Learning and Unsupervised learning and investigates Machine Learning algorithms in every one of the sub domains of Regression, Classification, Clustering, and so forth. It also talks about the engineering of calculations like Linear Regression, Logistic Regression, K-Means, K Nearest Neighbors, Hierarchical, DB Scan, Decision Tree, Random Forest Regression, and Random Forest classifier. Utilization of every algorithm to investigate the dataset will be displayed by carrying out it on renowned dataset model, and output of each piece of code is displayed with their preview. This section likewise takes care of the issue of predicting the future number of COVID-19 cases and the precision behind each model or algorithm is shown and investigated utilizing different measurements dependent on situation or issue articulation, for example, either issue is on forecast or order. This chapter does not focus on the solution of COVID-19 data analysis or expectation, rather it will be followed and will task different models dependent on need with conclusive target being clear comprehension of the Machine Learning algorithms and its execution in Python. 2023 Scrivener Publishing LLC. -
Depiction ofNifty Midcap Index Efficiency Using ARIMA
In recent years, the desirability of midcaps in Indian stock markets has received considerable attention from researchers, academicians, and financial analysts due to expectation of multi-bagger returns. The present study is undertaken to determine the market efficiency of Indian stock market using Nifty Midcap Index at High Frequency. The market efficiency of Nifty Midcap Index is determined using ARIMA technique. The fitted ARIMA model had a MASE value close to one. Hence, the findings suggest that the Nifty Midcap Index is inefficient. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Digitization of Monuments An Impact on the Tourist Experience with Special Reference to Hampi
The cultural heritage of India offers a deep examination of the country's political and historical evolution. Historical structures and monuments are among a nation's most valuable assets and a source of pride for Indian civilization. Monuments hold significant historical importance and exert a profound emotional influence on the community. Given the deterioration of culturally significant heritage monuments caused by factors such as weather, climate change, and human activity, as well as the threats these elements pose to numerous heritage sites of national and international significance, it is imperative to prioritize the recording, preservation, and conservation of these monuments. Events of cultural significance require comprehensive digital documentation and proper recording. As demonstrated by various programs and initiatives led by Prime Minister Narendra Modi, the government is committed to enhancing the visitor experience at monuments and museums. The primary aim of the current study is to better understand how cultural heritage sites are digitized and to assess the implications of this process for enhancing the tourist experience. To address the research objectives, a survey was conducted to analyze digital requirements. The digitization of significant cultural heritage sites is vital for the long-term sustainability of the tourism industry. Many methods will be adapted as resources permit, ensuring the industry's steady growth over time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.