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Cryptographic key distribution using artificial intelligence for data security and location privacy in VANET
Location privacy & data security in VANET are now becoming most important in todays paradigm of information age. Unauthorized access to location information of vehicles may pose a significant security threat, thus it is necessary to secure this information from intruders. In proposed work, Artificial intelligence based RF range approximation is used with multi key controlled cryptography for enhancement of location privacy and data security in service location protocol of VANETS. 2022 Taru Publications. -
Transmit Range Adjustment Using Artificial Intelligence for Enhancement of Location Privacy and Data Security in Service Location Protocol of VANET
IoT or the internet of things is the talk of the town topic being researched in the field of information technology for more than decade. It is being in deployment stage in various developing economics, to enable driverless automobiles in the field of VANET. It helps in preventing crashes and provides urgent medical assistance in emergency case. Data security and location privacy are becoming of most importance in present IT scenario. Unauthorized access to location information of vehicles may pose a significant security threat. So, it is necessary to secure the location information of the vehicle. The proposed work aims at enhancement of location privacy data security in service location protocol of VANET'S. The primary techniques to be employed include artificial intelligence-based RF range approximation for transmission range adjustment and receive RF strength based distance estimation for trusted node location perimeters approximation, dynamic adjustment of silence period of OBU (on based unit) in conjunction with radio/RF interrupt. The unauthorized access to location information of vehicles and need of its privacy is the motivation for this work. 2022 Shivkant Kaushik et al. -
Green Synthesized ZnO Nanoparticles as Biodiesel Blends and their Effect on the Performance and Emission of Greenhouse Gases
Pollution and global warming are a few of the many reasons for environmental problems, due to industrial wastes and greenhouse gases, hence there are efforts to bring down such emissions to reduce pollution and combat global warming. In the present study, zinc oxide nanoparticles are green synthesized using cow dung as fuel, through combustion. Synthesized material was characterized by FTIR, XRD, UV, and FESEM. The as-prepared ZnO-GS NPs were employed as a transesterification catalyst for the preparation of biodiesel from discarded cooking oil. The biodiesel obtained is termed D-COME (discarded cooking oil methyl ester), which is blended with 20% commercial diesel (B20). Additionally, this blend, i.e., B20, is further blended with varying amounts of as-prepared ZnO-GS NPs, in order to ascertain its effects on the quality of emissions of various greenhouse gases such as hydrocarbons, COx, NOx. Moreover, the brake thermal efficiency (BTHE) and brake specific fuel consumption (BSFC) were studied for their blends. The blend (B20) with 30 mg of ZnO-GS, i.e., B20-30, displays the best performance and reduced emissions. Comparative studies revealed that the ZnO-GS NPs are as efficient as the ZnO-C NPs, indicating that the green synthetic approach employed does not affect the efficiency of the ZnO NPs. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Covert Conditioning for Persistent Aggressive Behaviors: A Case Illustration
In psychotherapy practice and training, single case study design plays an indispensable role by effectively articulating the application of textbook knowledge, thereby bridging the gap between theory and practice. This article, on similar lines, illustrates one such successful example of the application of the classical behavioral technique of covert conditioning modified with a component of verbal challenging. A woman in her late-thirties reported with long-standing seemingly-resistant-to-treat symptoms of aggressive behavior of beating children. The client had a total of 10 daily sessions of 6090 minutes each. By the end of one week, she reported not beating children in this period. She felt extremely relieved because it had happened for the first time in 10 years. The intensity of anger had decreased drastically, and she was not shouting any longer. She had to discontinue sessions abruptly due to unavoidable circumstances. Although she was suggested to follow up the intensive sessions again, she was not able to do it due to feasibility issues. The improvement was maintained on follow-up visits after two weeks, four weeks, and three months. 2021 The Author(s). -
Engaged institution model: A faculty perspective
This paper attempts to build the engaged institution model from faculty perspective. Data was collected from 200 faculty members across disciplines, who were engaged in community engagement and social responsibility activities in one or the other ways. On analysis of the data, it was found that Instruction and Research, Facilitator, Scholarship factors contribute towards community engagement activities in higher educational institutions and that these factors contribute towards Faculty engagement, Student engagement and Community Engagement. All these factors create Engagement institution model. This work has an implications on theory, practice and policy. Service learning, as a pedagogical tool if implemented in HEIs can effectively bring all the influencing factors together and can help in creating an engaged institution. 2024, IGI Global. All rights reserved. -
Identification of coronary artery stenosis based on hybrid segmentation and feature fusion
Coronary artery disease has been the utmost mutual heart disease in the past decades. Various research is going on to prevent this disease. Obstructive CAD occurs when one or more of the coronary arteries which supply blood to myocardium are narrowed owing to plaque build-up on the arteries inner walls, causing stenosis. The fundamental task required for the interpretation of coronary angiography is identification and quantification of severity of stenosis within the coronary circulation. Medical experts use X-ray coronary angiography to identify blood vessel/artery stenosis. Due to the artefact, the image has less clarity and it will be challenging for the medical expert to find the stenosis in the coronary artery. The solution to the problem a computational framework is proposed to segment the artery and spot the location of stenosis in the artery. Here the author presented an automatic method to detect stenosis from the X-ray angiogram image. A unified Computational method of Jerman, Level-set, fine-tuning the artery structure, is developed to extract the segmented artery features and detect the arterys stenosis. The current experimental outcomes illustrate that this computational method achieves average specificity, sensitivity, Accuracy, precision and F-scores of 95%, 97.5%, 98%, 97.5% and 97.5%, respectively. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Advanced Computational Method to Extract Heart Artery Region
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal. 2022 Seventh Sense Research Group. -
A Novel Approach for Segmenting Coronary Artery from Angiogram Videos
This paper addresses the research focuses on coronary artery disease; it is one of the major heart diseases affecting the people all around the world in the recent era. This heart disease is primarily diagnosed using a medical test called angiogram test. During the angiogram procedure the cardiologist often physically selects the frame from the angiogram video to diagnose the coronary artery disease. Due to the waning and waxing changeover in the angiogram video, its hard for the cardiologist to identify the artery structure from the frame. So, finding the keyframe which has a complete artery structure is difficult for the cardiologist. To help the cardiologist a method is proposed, to detect the keyframe which has segmented artery from the angiogram video. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analysis of benchmark image pre-processing techniques for coronary angiogram images
Coronary Artery supplies oxygenated blood and nutrients to the heart muscles. It can be narrow by the plaque deposited on the artery wall. Cardiologists and radiologists diagnose the disease through visual inspection based on x-ray images. It is a challenging part for them to identify the plaque in the artery in the given imagery. By using image processing and pattern recognition techniques, a narrowed artery can be identified. In this paper, pre-processing methods of image processing are discussed with respect to coronary angiogram image(s). In general the angiogram images are affected by device generated noise / artifacts; pre-processing techniques help to reduce the noise in the image and to enhance the quality of the image so that the region of interest is sensed. The main objective of the medical image analysis is to localize the region of interest by removing the noise. It is essential to find the structure of the artery in the angiogram image, for that preprocessing is useful. 2021 IEEE. -
A Perspective on Challenges and Opportunities of Supply Chain Management
Global Journal of Arts and Management Vol. 2, No. 3, pp. 227 - 231, ISSN No. 2249-2658 -
Stocks and throughput Accounting on Material Management and its Impact on Cost Management
Global Journal of Arts and Management, Vol. 2, No. 3, pp. 244-246, ISSN No. 2249-2658 -
An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model
Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE. -
Nouveau shoppers buying behavior pattern and perception towards luxury brands
The customer perception towards purchasing luxury brands has various psychological patterns and the behviour towards purchasing such brands differs accordingly. The main objective of the study is to map the nouveau shoppers mind-set towards shopping malls and to analyze the buying behavior pattern and perception towards luxury brand on shopping malls. For this purpose a sample of 130 was collected from the respondents were percentage analysis, descriptive statistics, Kruskall Wallis test and Oneway anova were used as tools to analye the data. The conclusion is that shopping malls have higher potentiality to pull the customers to visit their places but the conversion of making every customers purchasing in the mall is based on various factors of each individual shops. The conversion towards making the consumers purchasing the products can be done to attractive displays and understanding the mindset of modern shoppers towards various products and brand. 2020 Webology Center. -
Smart Home Systems Using Wireless Sensor Network - A Comparative Analysis
International Journal of Computer Engineering & Technology, Vol-3 (3), pp. 94-103. ISSN-0976-6367 -
Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living
Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities. 2019 Procedia Computer Science. All rights reserved. -
Ambient monitoring in smart home for independent living
Ambient monitoring is a much discussed area in the domain of smart home research. Ambient monitoring system supports and encourages the elders to live independently. In this paper, we deliberate upon the framework of an ambient monitoring system for elders. The necessity of the smart home system for elders, the role of activity recognition in a smart home system and influence of the segmentation method in activity recognition are discussed. In this work, a new segmentation method called area-based segmentation using optimal change point detection is proposed. This segmentation method is implemented and results are analysed by using real sensor data which is collected from smart home test bed. Set of features are extracted from the segmented data, and the activities are classified using Naive Bayes, kNN and SVM classifiers. This research work gives an insight to the researchers into the application of activity recognition in smart homes. Springer Nature Singapore Pte Ltd. 2019. -
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Financial analytical usage of cloud and appropriateness of cloud computing for certain small and medium-sized enterprises
The term "cloud computing"refers to a novel approach of providing useful ICTs to consumers over the internet on an as-needed and pay-per-usage basis. Businesses may streamline internal processes, increase contact with customers, and expand their market reach with the aid of cloud computing, which provides convenient and inexpensive access to cutting-edge information and communication technologies. Developing economies like India's present unique problems for small and medium-sized businesses (SMEs), such as a lack of funding, an inadequate workforce, and inadequate information and communication technology (ICT) use. Various advantages offered by current information and communication technology solutions are unavailable to SMEs because of these limitations. If small and medium-sized enterprises (SMEs) are seeking to enhance their internal operations, communication with customers and business partners, and market reach using current information and communication technology (ICT) solutions, cloud computing might be a good fit for them. Therefore, SMEs are particularly well-served by cloud computing. Companies with a lack of capital, personnel, or other resources to deploy and use appropriate ICTs may greatly benefit from cloud computing, and the public cloud in particular. 2024 Author(s). -
Design and performance analysis of eight channel demultiplexer using 2D photonic crystal with trapezium cavity
In this work, an eight-channel dense wavelength division multiplexing demultiplexer is designed with a 2D photonic crystal triangular lattice. The proposed demultiplexer consists of a centre bus waveguide, an isosceles trapezium resonant cavity, and an eight-circular ring cavity (CR1, CR2, CR3, CR4, CR5, CR6, CR7, and CR8). The point defect resonant cavity consists of seven rods to drop different wavelengths from eight cavities, each of eight drop waveguides. The design is very simple to realise. The finite difference time domain and plane wave expansion method methods were used to analyse the proposed designs band structure and transmission spectrum. The resonant wavelengths are 1.5441 ?m, 1.5443 ?m, 1.544 49 ?m, 1.5447 ?m, 1.5449 ?m, 1.5451 ?m, 1.5453 ?m, and 1.5455 ?m respectively. The proposed device provides a high-quality factor, transmission efficiency, and low crosstalk. The devices footprint is 490.0 ?m2, which can be easily incorporated into photonic integrated circuits. 2023 IOP Publishing Ltd. -
Evaluation of phytoconstituents of Triticum aestivum grass extracts on nutritional attributes, antioxidant, and antimicrobial activities against food pathogens with molecular in silico investigation
The plant-based medicine and diet is gaining importance in recent days. The consumption of Triticum aestivum grass in the form of juice and tablets is increasing among common people. The present study elaborates on the nutritional, antioxidant, and antimicrobial potential of a nongenetically modified type of T. aestivum grass, along with the evidence of molecular docking studies. The T. aestivum grass extracts like decoction, aqueous, ethanol, and chloroform were subjected to preliminary phytochemical tests, quantitative estimation, antioxidant analysis, and antimicrobial activity determination. The ethanolic extract that had good antioxidant and antimicrobial potential was subjected to gas columnmass spectroscopy (GCMS) analysis and the compounds identified were docked against the antioxidant and antimicrobial receptors. The decoction and aqueous extracts performed well in preliminary qualitative tests with the presence of most of the phytochemicals tested. The decoction, aqueous, and ethanolic extracts possessed good concentrations of the phytochemicals. The decoction had about 210.839.16 and 154.160.33mg/g of carbohydrates and proteins, respectively, while the aqueous extract had about 10.910.08mg/g of amino acids and the ethanolic extract had about 52.51.4mg/g of phenolic content, which were the highest concentration of the phytochemicals observed among the extracts. Along with phytochemical potential, good antioxidant potential in the DPPH and ABTS by decoction as well as ethanolic extract with nearly 40 and 90% inhibition, respectively, and in FRAP by aqueous extract with maximum OD value. The ethanolic extract exhibited the best inhibition potential against the Staphylococcus aureus about 281mm, Pseudomonas aeruginosa with 202mm, Bacillus cereus at 201mm by the ethanolic extract at 200?g concentration, and Aspergillus fumigatus and A. niger at 150mm by the aqueous extract at 200?g concentration. The GCMS analysis revealed the presence of terpenoids, alkaloids, and phenols, which on docking had highest binding capacity toward the antioxidant and antimicrobial receptors. 2023 The Authors. Food Frontiers published by John Wiley & Sons Australia, Ltd and Nanchang University, Northwest University, Jiangsu University, Zhejiang University, Fujian Agriculture and Forestry University.