Browse Items (5511 total)
Sort by:
-
An energy efficient approach of deep learning based soft sensor for air quality management
Monitoring environmental pollution is emerging as a recent study area especially in urban and highly polluted industrial areas. This field deploys many chemical analysis models and data driven models through soft sensors. But bio indicators are a more feasible, cost effective and precise monitoring model, which are rarely explored. This paper is based on growth monitoring of Cryptogams, a bio indicator which is capable of directly reflecting the pollution levels in the region of growth. A novel enhanced and energy efficient deformable active contour model is introduced to trace the development of transplanted Cryptogams at various sites with diverse pollution levels. The vegetative development of Cryptogams is monitored for duration of two weeks. The proposed energy efficient contour tracing model proves its superiority in precise tracing of the Cryptogam development, thus aiding in accurate pollution monitoring. The VGG 16 architecture built using deep convolutional neural network by constructing stacks of filters. VGG 16 architecture showed high performance when compared with other existing models. The accuracy is compared with the Ant colony optimization using GVF. 2022 The Authors -
An energy efficient authentication scheme based on hierarchical ibds and eibds in grid-based wireless sensor networks
Wireless sensor network is a peculiar kind of ad hoc network, consists of hundreds of tiny, resource constrained as sensor nodes. Clustering is a demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster-based routing algorithm is proposed on which hierarchical identity-based digital signature (IBDS) and enhanced-identity-based digital signature (EIBDS) scheme is concerning in grid-based wireless sensor networks. Firstly we form clusters using multi-parameters-based type-2 fuzzy logic algorithm. This paper proposes an improved ant colony optimisation algorithm, which optimises the energy consumption on data transfer in a WSN. Each node in a sensor network is authenticated using elliptic curve cryptography (ECC). After a set of simulation tests on NS-3 simulator, our proposed work achieves good performance for various metrics. Copyright 2020 Inderscience Enterprises Ltd. -
An Energy Efficient Node Scheduling based Congestion Control Scheme for WSN Multicasting
Wireless Sensor Network (WSN) is the most preferred technology for communication in resource constrained environments. They offer high-quality data propagation with limited delay. Sensor Network can be established with the help of self-configurable nodes to monitor various physical phenomenon. Multicasting in WSN results in low communication control overhead but may lead to congestion, which results in data loss, redundant transmissions, poor throughput and reduced network lifetime. In this paper, we propose a protocol to estimate the Degree of Congestion (CD) at each node to ensure load balance and avoid further congestion within the network. It is demonstrated that the proposed scheme is better compared with existing congestion control schemes in terms of end-to-end delay and energy efficiency. 2020 G. Raja Vikram et al., licensed to EAI -
An Enhanced Approximation Algorithm Using Red Black Tree and HashMap for Virtual Machine Placement Problem
The virtual machine placement problem (VMPP) is an np-hard optimization problem in cloud computing that involves efficiently allocating virtual machines (VMs) to physical hosts in such a way that the resource wastage is minimized, and resource usage is optimal while ensuring adequate performance. This paper proposes a modified best-fit approximation algorithm using Red Black Tree (RBT) and HashMap for addressing the VMPP with enhanced computational efficiency in such a way that the active hosts in a given data center remains minimum possible. The proposed algorithm builds up on the existing best-fit approximation algorithm by using RBT and HashMap. The proposed approach considers various attributes such as CPU utilization, memory requirements, and network bandwidth while allocating virtual machines. To evaluate the performance the simulation is done in cloudsim environment with PlanetLab workload. Test cases are considered in both homogeneous and heterogeneous environments and results are taken. Comparative analyses were performed against existing benchmark algorithms in terms of time complexity and resource usage in terms of active hosts. The results demonstrate that the proposed algorithm outperforms the existing algorithms and guarantees time complexity of O(log n) and give better results compared to other algorithms. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Enhanced Automation Analysis for Structural Algorithm in Agro-Industries Using IoT
The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze data and use this knowledge to take specific actions to help farmers and producers maximize their production and profitability. This algorithm provides an unprecedented level of precision, accuracy and control over the agricultural environment, allowing greater efficiency and optimization in farming practices. It enables monitoring, scheduling, and control of different agro-ecosystem components, such as water, soil, fertilizer, light, humidity, temperature, soil pH and crop growth. The algorithm can also point to general trends and patterns in the environment, as well as offer timely advice to farmers in response to real-time conditions. The algorithm is also capable of automatically diagnosing and responding to unexpected problems, which can help prevent costly mistakes and excessive waste of water, fertilizer, energy, etc. 2023 by the authors. -
An enhanced biometric attendance monitoring system using queuing petri nets in private cloud computing with playfair cipher
Every educational institutions needs to analyse and monitor participation. Educationists believe that there should be a fair number of students available in the majority of their classes. In colleges participation is used a measure of consistency. To deal with this kind of a challenging situation, biometric based participation monitoring framework is being proposed. This proposed method with the assistance of face recognition will help in maintaining every detail about the present students in a classroom save the same in the class database. The camera captures the image of students and compares them with the existing visual data available in the database. In case, the software is not able to find a match for the captured data in the student database, the particular student is marked as absent. Queuing Petri nets help in fulfilling customised demands of various institutions along with providing better performance in terms of hold up time. With the application of this technology, classroom participation is recorded and saved every hour. The database is accessed and maintained using cloud services and necessary security measured are incorporated as provided by major private cloud service providers with playfair cipher technique. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
An enhanced network intrusion detection system for malicious crawler detection and security event correlations in ubiquitous banking infrastructure
Purpose: In the recent era, banking infrastructure constructs various remotely handled platforms for users. However, the security risk toward the banking sector has also elevated, as it is visible from the rising number of reported attacks against these security systems. Intelligence shows that cyberattacks of the crawlers are increasing. Malicious crawlers can crawl the Web pages, crack the passwords and reap the private data of the users. Besides, intrusion detection systems in a dynamic environment provide more false positives. The purpose of this research paper is to propose an efficient methodology to sense the attacks for creating low levels of false positives. Design/methodology/approach: In this research, the authors have developed an efficient approach for malicious crawler detection and correlated the security alerts. The behavioral features of the crawlers are examined for the recognition of the malicious crawlers, and a novel methodology is proposed to improvise the bank user portal security. The authors have compared various machine learning strategies including Bayesian network, support sector machine (SVM) and decision tree. Findings: This proposed work stretches in various aspects. Initially, the outcomes are stated for the mixture of different kinds of log files. Then, distinct sites of various log files are selected for the construction of the acceptable data sets. Session identification, attribute extraction, session labeling and classification were held. Moreover, this approach clustered the meta-alerts into higher level meta-alerts for fusing multistages of attacks and the various types of attacks. Originality/value: This methodology used incremental clustering techniques and analyzed the probability of existing topologies in SVM classifiers for more deterministic classification. It also enhanced the taxonomy for various domains. 2021, Emerald Publishing Limited. -
An Enhanced Pathfinder Algorithm for Optimal Integration of Solar Photovoltaics and Rapid Charging Stations in Low-Voltage Radial Feeders
Most low-voltage (LV) feeders have large distribution losses, poor voltage profiles, and inadequate voltage stability margins owing to their radial construction and high R/X ratio branches, and they may not be able to handle substantial solar photovoltaics (SPVs) and EV penetration. Thus, optimal integration of SPVs and rapid charging stations (RCSs) can solve this problem. This paper offers an extended pathfinder algorithm (EPFA) with guiding elements and three followers' life lifestyle procedures based on animal foraging, exploitation, and killing. First, the EV load penetration was used to evaluate the LV feeder performance. Subsequently, the required RCSs and SPVs were appropriately integrated to match the EV load penetration and optimise feeder performance. An Indian 85-bus real-time system was used for simulations. The losses and GHG emissions increased by 150% and 80%, respectively, without the SPVs and RCS for zero-to-full EV load penetration. RCSs allocation alone reduced the losses by 40.1%, whereas simultaneous SPVs and RCSs allocation reduced the losses by 66%. However, the GHG emissions decreased by 13.7% and 54.33%, respectively. This study shows that SPVs and RCS can enhance the LV feeder performance both technically and environmentally. In contrast, EPFA outperformed the other algorithms in terms of the global solution and convergence time. The Author(s). -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect
Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work. 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). -
An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer
Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
An enhancing reversible data hiding for secured data using shuffle block key encryption and histogram bit shifting in cloud environment
Nowadays there are numerous intruders trying to get the privacy information from cloud resources and consequently need a high security to secure our data. Moreover, research concerns have various security standards to secure the data using data hiding. In order to maintain the privacy and security in the cloud and big data processing, the recent crypto policy domain combines key policy encryption with reversible data hiding (RDH) techniques. However in this approach, the data is directly embedded resulting in errors during data extraction and image recovery due to reserve leakage of data. Hence, a novel shuffle block key encryption with RDH technique is proposed to hide the data competently. RDH is applied to encrypted images by which the data and the protection image can be appropriately recovered with histogram bit shifting algorithm. The hidden data can be embedded with shuffle key in the form of text with the image. The proposed method generates the room space to hide data with random shuffle after encrypting image using the definite encryption key. The data hider reversibly hides the data, whether text or image using data hiding key with histogram shifted values. If the requestor has both the embedding and encryption keys, can excerpt the secret data and effortlessly extract the original image using the spread source decoding. The proposed technique overcomes the data loss errors competently with two seed keys and also the projected shuffle state RDH procedure used in histogram shifting enhances security hidden policy. The results show that the proposed method outperforms the existing approaches by effectively recovering the hidden data and cover image without any errors, also scales well for large amount of data. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
An equal split triple-band wilkinson power divider employing extended cross shaped microstrip line /
Microwave and Optical Technology Letters, Vol.60, Issue 10, pp.2488-2492. -
An ethnographic expose of Mithun-human interrelationship among the Kuki community of Northeast India
Unrestrained consumption and a lack of a proper breeding ecosystem have depleted the variety and species count of mithun (Bos frontalis). Indigenous Kuki tribes have a unique relationship with mithun, reared in the semi-domestic countryside. For the Kuki community, a mithun is used during community festivals, as a bride price in marriages, to settle disputes, in land-deed covenants, and at death ceremonies. Mithun-human interrelationship lessens poverty, empowers community survival, guarantees the completion of critical cultural obligations, and maintains marital bonds in the Kuki community. The head of a mithun signifies solemnity and celebration in many cultural underpinnings. A white cock, a dog, a goat, a pig, and a mithun were sacrificial elements to appease the unseen spirits for good health and prosperity. While some Indigenous practices have faded with the arrival of Christianity, the cultural involvement of mithun persists to this date. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
An Examination of the Challenges Associated with Applying Artificial Intelligence Techniques to Specific Management Problems
Artificial intelligence (AI) holds immense promise in revolutionizing management practices across various sectors, offering solutions to complex problems and optimizing decision-making processes. However, the application of AI techniques to management problems is not without its challenges. This examination delves into the multifaceted hurdles encountered when integrating AI into management frameworks, highlighting key obstacles and potential avenues for overcoming them.AI algorithms heavily rely on large volumes of high-quality data for effective training and decision-making. Yet, many management domains grapple with disparate data sources, inconsistencies, and incomplete datasets, hindering the performance and reliability of AI systems. Furthermore, the dynamic nature of management problems poses a significant challenge to AI implementation. Management environments are characterized by evolving trends, uncertainties, and unforeseen disruptions, rendering static AI models inadequate in adapting to changing conditions. Hence, the development of agile AI systems capable of continuous learning and adaptation becomes essential for addressing the dynamic nature of management challenges. 2024, Collegium Basilea. All rights reserved. -
An Expected Model of Management Program in India
Pravara Management Review, Vol 15, Issue 2, pp. 17-23, ISSN No. 0975-7201 -
An experimental investigation to study the performance and emission characteristics of n-butanol-gasoline blends in a twin spark ignition engine
The need of a substitute for the fossil fuels has gained maximum importance in the recent days with the depletion of fossil fuels, increasing vehicle population, enforcement of strict pollution norms to ensure a better environment for the present and future generations. Researchers around the world have investigated many fuels for IC engines and have found that alcohols exhibit properties that closely resemble the properties of gasoline. Alcohols form a stable mixture with gasoline in almost all proportions. This property of alcohol has increased its popularity as a fuel blend with gasoline. This paper aims at presenting the performance characteristics of a twin spark ignition engine fuelled with the blends of n-butanol-gasoline. In this investigation, pure gasoline (B00) and blends of gasoline with n-Butanol forms the fuel for twin spark ignition engine. The use of B35 blend, lower carbon monoxide emissions, lower unburnt hydrocarbon and lower nitrogen oxide emissions are observed as compared to pure gasoline. With these investigational results, one can arrive at the conclusions that with the use of higher blends of n-butanol-gasoline, the emission of the regulated emissions are reduced and are seen to be optimal with B35 in a twin spark ignition engine. TJPRC Pvt. Ltd. -
An exploration of 'pull' and 'push' motivational factors among transgender entrepreneurs
To date, studies have focused on the men and women entrepreneurs and the gender difference in motivations among cisgender entrepreneurs. The study aims to determine whether a transgender individual entrepreneur is motivated through a push motivational factor or a pull motivational factor. This study employs a qualitative approach uses face-to-face interviews and a semi-structured interview with a sample size of 16 transgender entrepreneurs in India. It was found that the participants in this study were motivated by both push and pull factors. The motivational factors, which add to the knowledge of already existing push and pull factors, were to forego begging and commercial sex work, to break stereotypes, to create a business opportunity for other transgender individuals, to earn respect from society, to prove entrepreneurship is non-binary, to be a role model for other transgender individuals and to the society. In contrast, the push motivational factors were the limited opportunities, support received from society, the hijra guru, media, government support, family, friends, landlords, NGOs and another push motivational factor was the exhibitions conducted exclusively for the transgender individual entrepreneurs. 2025 Inderscience Enterprises Ltd. -
An exploration of attitudes toward dogs among college students in Bangalore, India
Conversations in the field of anthrozoology include treatment and distinction of food animals, animals as workers versus pests, and most recently, emerging pet trends including the practice of pet parenting. This paper explores attitudes toward pet dogs in the shared social space of urban India. The data include 375 pen-and-paper surveys from students at CHRIST (Deemed to be University) in Bangalore, India. Reflecting upon Serpells biaxial concept of dogs as a relationship of affect and utility, the paper considers the growing trend of pet dog keeping in urban spaces and the increased use of affiliative words to describe these relationships. The paper also explores potential sex differences in attitudes towards pet and stray dogs. Ultimately, these findings suggest that the presence of and affiliation with pet dogs, with reduced utility and increased affect, is symptomatic of cultural changes typical of societies encountering the second demographic transition. Despite this, sex differences as expected based upon evolutionary principles, remain present, with women more likely to emphasize health and welfare and men more likely to emphasize bravery and risk taking. 2019 by the authors. Licensee MDPI, Basel, Switzerland.