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Development of smart energy monitoring using NB-IOT and cloud
IoT-based applications are growing in popularity nowadays because they offer effective answers to numerous current problems. In this research, With the aim of decreasing human efforts for monitoring the power units and increasing users' knowledge of excessive electricity usage, an IoT-based electric metre surveillance system utilising an Android platform has been developed. With the help of an Arduino Uno and an optical sensor, the electric analyser pulse is captured. To reduce human mistake and the expense of energy usage, a low-cost wireless network of sensors for digital energy metres is implemented alongside a smartphone application that can autonomously read the metre of the unit. In this research, an intelligent power monitoring system with effective communication modules has been developed to make wise use of the electricity. The controller, NB-IoT connection module, and cloud are the three main components of an IOT-based smart energy metre system. The controller is essential for maintaining the functionality of each component. This solution reduces the need for human involvement in electricity maintenance by connecting energy metres to the cloud using an NB-IoT communication module. The IoT-based metre reading system in the proposed work is created to monitor and analyse the metre reading, and the service provider can cut off the source of electricity whenever the customer fails to pay the monthly bill. It also eliminates the need for human intervention, provides accurate metre reading, and guards against billing errors. The proposed SPM improves the overall accuracy ranges of 7.42, 27.83, and 20% better than DR, OREM, and SLN respectively. 2023 -
An investigation and analysis on automatic speech recognition systems
A crucial part of a Speech Recognition System (SRS) is working on its most fundamental modules with the latest technology. While the fundamentals provide basic insights into the system, the recent technologies used on it would provide more ways of exploring and exploiting the fundamentals to upgrade the system itself. These upgrades end up in finding more specific ways to enhance the scope of SRS. Algorithms like the Hidden Markov Model (HMM), Artificial Neural Network (ANN), the hybrid versions of HMM and ANN, Recurrent Neural Networks (RNN), and many similar are used in accomplishing high performance in SRS systems. Considering the domain of application of SRS, the algorithm selection criteria play a critical role in enhancing the performance of SRS. The algorithm chosen for SRS should finally work in hand with the language model conformed to the natural language constraints. Each language model follows a variety of methods according to the application domain. Hybrid constraints are considered in the case of geography-specific dialects. 2024 by author(s). -
Hybrid cryptography security in public cloud using TwoFish and ECC algorithm
Cloud computing is a structure for rendering service to the user for free or paid basis through internet facility where we can access to a bulk of shared resources which results in saving managing cost and time for large companies, The data which are stored in the data center may incur various security, damage and threat issues which may result in data leakage, insecure interface and inside attacks. This paper will demonstrate the implementation of hybrid cryptography security in public cloud by a combination of Elliptical Curve Cryptography and TwoFish algorithm, which provides an innovative solution to enhance the security features of the cloud so that we can improve the service thus results in increasing the trust overthe technology. 2019 Institute of Advanced Engineering and Science. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system
The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Dirichlet Feature Embedding with Adaptive Long Short-Term Memory Model for Intrusion Detection System
Intrusion Detection System is applied in the network to monitor the network activity and detect the intruder to protect the user data. Various existing models have been applied in the intrusion detection system and have the limitations of high False Alarm Rate (FAR), overfitting problem and data imbalance problem. In this research, Dirichlet Feature Embedding based Adaptive Long Short Term Memory (DFE-LSTM) model is proposed to improve the efficiency of the intrusion detection. The Dirichlet Feature Embedding (DFE) method is applied to effectively represent the feature to analysis the multi-variate of the input data. The enhanced Adaptive Long Short Term Memory (ALSTM) model is applied to select the optimal parameter for the LSTM model to improve the learning rate. The proposed DFE-ALSTM model is compared to three datasets such as UNSW-NB15, NSL-KDD and Kyoto 2006+ for evaluate the efficiency. The proposed DFE-ALSTM model has the accuracy of 94.32 % and existing NB-SVM has 93.75 % accuracy in intrusion detection on UNSW-NB15 dataset. 2022, Success Culture Press. All rights reserved. -
A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities
Nowadays, the smart grid has demonstrated a great ability to make life easier and more comfortable given recent advances. This paper studies the above issue from the perspective of two important and very useful smart grid applications, i.e., the advanced metering infrastructure and demand response using the instrumentality of a set of well-known scheduling algorithms, e.g., best-channel quality indicator, log rule, round robin, and exponentialproportional fairness to validate the performance. To increase the data transmission bandwidth, a new concept of optical wireless communication known as free-space optical communication (FSO) system based on microring resonator (MRR) with the ability to deliver up to gigabit (line of sight) transmission per second is proposed for the two studied smart grid applications. The range between 374.7 and 374.79THz frequency band was chosen for the generation of 10 successive-carriers with a free spectral range of 8.87GHz. The ten multi-carriers were produced through drop port of the MRR. The results show up to 10 times bandwidth improvement over the radius as large as 600m and maintain receive power higher than the minimum threshold (? 20dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Optimal design of controller for automatic voltage regulator performance enhancement: a survey
For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SGs Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant
Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems. 2024 -
The Hubble tension: Change in dark energy or a case for modified gravity?
Recently, much controversy has been raised about the cosmological conundrum involving the discrepancy in the value of the Hubble constant as implied by Planck satellite observations of the CMBR in the early Universe and that deduced from other distance indicators (for instance using standard candles like supernovae, tip of the red giant branch, etc.) in the present epoch. The Planck estimate is about 67km-1Mpc-1, while that deduced from distance indicators at the present epoch is around 73-74km-1Mpc-1. Also the independent determination of the local value of the Hubble constant based on a calibration of the tip of the red giant branch and applied to Type Ia supernovae found a value of 69.8km-1Mpc-1. Here we propose a modification of the gravitational field on large scales as an alternate explanation for this discrepancy in the value of the Hubble constant as implied in the above-mentioned method, i.e., by Planck observations of the CMBR in the early Universe, and that deduced from other distance indicators in the present epoch. 2021, Indian Association for the Cultivation of Science. -
Reimagining Future of Future by redesigning Talent Strategy in the Age of Distraction and Disruption
The coronavirus 2019 (COVID-19) pandemic promoted the development of Industry 4.0 leading to the fifth industrial revolution (Industry 5.0). It brought in new ways of working and the role of the office in the future. It redesigned the workplace to support organizational priorities and resize the footprint creatively. Digitalization and globalization have sparked radical shifts in how employees live and work. In an age of digital disruption, companies and HR leaders are forced to revise organizational on how they organize, recruit, develop, manage and engage the 21st-century workforce. The big questions are: how can HR help business leaders reconstruct the workforce of the future? What effort has the company take to change future work and their workforce today so that it looks different 15 years later? Organizational agility, careers and learning disruption, talent disruption, rethinking performance management and people analytics in addition to creating the right structure, analysis, and standardized people metrics are the key to success and critical drivers to design talent strategy. This study aims to identify the magic ingredient (or strategies) behind managing an organization's talent in creating business success. We further examined and mathematically modelled these strategies in attracting and retaining high-quality employees, developing their skills, and continuously motivating them to improve their performance in the age of distraction and disruption. 354 employees from IT companies participated in the survey. The findings of the study show, as expected, that a compelling employer brand is the most effective talent management strategy of all when it combines three key drivers: organizational culture, organization goodwill and competition for talent. Gender was statistically, significantly and positively associated with the imperatives to reset the future of work agenda. 2021. All Rights Reserved. -
Environmental Management: Pragmatic Suitability of Low Cost Activated Carbon in Lead (II)Ion Removal by Continuous Mode of Adsorption
Heavy metals such as chromium, lead, and arsenic are usually present in trace amounts in natural waters but many of them are toxic even at very low concentrations. An increasing quantity of heavy metals in our resources is currently an area of greater concern, especially since a large number of industries are discharging their metal containing effluents into freshwater without any adequate treatment. Activated carbons show a significant ability in removing heavy metal ions from an aqueous solution by adsorption, which has been examined by many researchers. Activated carbon derived from Manilkarazapota tree-wood (MZTWAC), which was found to be a suitable adsorbent for the removal of lead ions through continuous adsorption mode, was examined in this paper. A breakthrough curve has been plotted to find the effect of initial concentration and adsorbent bed height in the adsorption of lead (II)ion through MZTWAC. The breakthrough time and the saturation time increased as the initial concentration increased from 40 mg.L-1 to 60 mg.L-1. The saturation time was in the incremental mode when the bed height was increased from 5 cm to 7 cm bed thickness for 40 mg.L-1 concentration. Adams-Boharts model perfectly fits with this fixed-bed column in the removal of lead(II) from an aqueous solution using MZTWAC. Activated carbon derived from MZTWAC is better suited for the purpose of detoxifying metal-contaminated wastewater. 2021 Technoscience Publications. All rights reserved. -
Ethical living and work self efficacy beliefs of academicians of higher education in ASIA: A key determinant of one's belief in one's ability to achieve the desired result in a precise state of affairs
Ethical academicians are perfectly virtuous. They always strive for greater virtue and follow strictly the moral stands of their profession. The ethical living and self-efficacy are important to them because of being fair and honest in their academics. Determinants of ethics include knowledge, values, attitude and intention. The domain-specific framework developed by Verbeke et al. (2004) has been considered as fundamental for identifying the dimensionality of work Self-efficacy and ethical challenges of academicians. A comprehensive literature review is undertaken regarding the concept of work Self-efficacy to assess workers' confidence and their ethical living in the workplace. This article examines theoretically and analytically the antecedent processes and information cues involved in the formation of work self-efficacy. Theoretical and numerical analysis of the key determinants of work self-efficacy increases the understanding of moral values, truthful fair and honest. Factors which decisively affect ethical living were identified from literature collected from the academicians who are working in the Five Regions of Asia-Central Asia (Tajikistan, Uzbekistan, Kazakhstan, Turkmenistan, Kyrgyzstan) East Asia (China, Mongolia, North Korea, South Korea, Japan, Hong Kong, Taiwan, Macau) South Asia (Sri Lanka, Bangladesh, India, Afghanistan, Pakistan, Bhutan, Nepal, the Maldives) through Google classroom. Methods of Statistical Analysis of self-efficacy data are descriptive statistics, Pearson Correlation Coefficient and Kolmogorov-Smirvnos normality test and KruskalWallis one-way analysis of variance and Principal Component Analysis. Positive, mastery experiences give academicians a sense of accomplishment when they have faced a challenge ethically. Positive Zeal during Academic interaction, vicarious experiences that occur when academician see others succeed and feel an increased sense of their own ability to succeed. Sincere & deeper self, mingling with students, Social persuasion increase a teachers sense of confidence and ability to succeed. A proper plan of action has drawn special attention, and inferences pertaining to future research are discussed at the end of the critique. 2019, Sciedu Press. All rights reserved. -
Death-worlds, Necropolitics and Decoloniality Colonial Negotiations in Mah
The boundaries of sovereignty are mostly relegated to modern and late modern political thoughts that focus on biopolitical and democratic theories. This paper marks a shift of sovereign subjectivity to the interstitial spaces of life and death of the colonial subjects. Through the study of the necropolitics of colonial control in the erstwhile French colony of Mah as narrated in the novel On the Banks of the Mayyazhi, this paper argues that colonial subjectivity and the idea of sovereignty have decentred itself from the traditional notions of political control and violence to newer avenues of life and death. The perusal of the decolonial approach to necropolitics will examine how colonial logic has shaped the idea of sovereignty. 2024 Economic and Political Weekly. All rights reserved. -
Monopsonistic exploitation in contract farming: Articulating a strategy for grower cooperation
Contract farming has been considered a new hope to instil dynamism in third world agriculture. However, there remains serious concern whether small peasants will be able to benefit from this system since buyers may often be a single large or at most, few large corporations, a typical case of monopsony. In this paper we question the basis of the fears that are often raised in the literature. A clear analytical approach to understanding the (economic) meaning of monopsony helps us articulate a strategy for grower cooperation that could effectively deal with monopsony power in contract farming systems. Copyright 2007 John Wiley & Sons, Ltd. -
Stability of porous medium convection in polarized dielectric fluids with non-classical heat conduction
International Journal of Mathematical Archive Vol.4, Issue 4, pp.136-144, ISSN No. 2229-5046 -
A Pilot Feasibility Study of Reconnecting to Internal Sensations and Experiences (RISE), a Mindfulness-Informed Intervention to Reduce Interoceptive Dysfunction and Suicidal Ideation, among University Students in India
Although 20% of the worlds suicides occur in India, suicide prevention efforts in India are lagging (Vijayakumar et al., 2021). Identification of risk factors for suicide in India, as well as the development of accessible interventions to treat these risk factors, could help reduce suicide in India. Interoceptive dysfunctionor an inability to recognize internal sensations in the body has emerged as a robust correlate of suicidality among studies conducted in the United States. Additionally, a mindfulness-informed intervention designed to reduce interoceptive dysfunction, and thereby suicidality, has yielded promising initial effects in pilot testing (Smith et al., 2021). The current studies sought to replicate these findings in an Indian context. Study 1 (n = 276) found that specific aspects of interoceptive dysfunction were related to current, past, and future likelihood of suicidal ideation. Study 2 (n = 40) was a small, uncontrolled pre-post online pilot of the intervention, Reconnecting to Internal Sensations and Experiences (RISE). The intervention was rated as highly acceptable and demonstrated good retention. Additionally, the intervention was associated with improvements in certain aspects of interoceptive dysfunction and reductions in suicidal ideation and eating pathology. These preliminary results suggest further testing of the intervention among Indian samples is warranted. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Exploring the economy of creativity and culture in the light of Industry 5.0: a systematic literature review of the setup ofcreative industries
Purpose: This study aims at stirring up the existing research conducted in the field of creative economy (CE) and also in the context of Industry 5.0. CE encompasses all the creative industries/businesses which form a major part of the knowledge-based economy. The functionalities of these setups, their global trends and developments are to be assessed for a better understanding of its present circumstances and its prospective opportunities by augmenting Industry 5.0 and its core principles. This provides a comprehensive illustration to enhance the economic, social, creative and sustainable performances of the creative industry. In addition, the study also seeks to identify the dynamics of creative units and how it could highly contribute to the glorification of the creative and cultural history in the Indian economic backdrop. Design/methodology/approach: The study adopts a systematic literature review process to fulfill the research objective. Four critical databases in Scopus such as Emerald Insight, Springer Link, Sage Publications and Taylor and Francis have been chosen for the review process. Following the critical literature review process, the chosen articles from each database have been retrieved for an exhaustive analysis within a time frame of 20132023 to evaluate the research evolution on the subject area. Findings: The paper identified various research dimensions and perspectives of the researchers in the area of study. This gives a platform to extensively evaluate the capabilities and functionalities of the sector for strategy building and enhancing returns from the sector. Research limitations/implications: As the methodology was restricted to top 5 articles from 5 important databases, the study was limited to only those articles and the other open-access peer-reviewed articles/journals/databases have not been considered which is a major limitation. Alongside, as the time frame was restricted for a period of 10years and only English language papers were chosen, prior study has not been considered, which is also a key limitation to the study. Practical implications: Policymakers, i.e. government and institutions, can understand the existence and contribution of the CE in different geographical regions for a specified period of time. This helps them understand the new revolution, Industry 5.0, and how they could merge their concepts to bring innovations in the sector and support in building sustainable cities in the emerging economies. Originality/value: As the paper works on bringing out the viewpoints of multiple authors and research works, it is considered to be a novel study as none of the previous studies, especially systematic literature review works, have been done only in high-quality journals of Scopus database. Therefore, the study holds high-quality information which can be significantly used by creative business units. 2024, Emerald Publishing Limited. -
Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review
Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of postoperative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G.