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Optimal Charging Strategy for Spatially Distributed Electric Vehicles in Power System by Remote Analyser
The burden on the consumer for the price of fuel for classic vehicles is the root cause for the emergence of the fast growing trend in the power driven vehicles or electric vehicles. Less acceptance of electric vehicles by the customers and the hesitancy to replace traditional fuel powered vehicles by considering the economic factor is a major concern that existing in the current scenario. Therefore, for the proper balancing of the load with respect to the power available among different neighbouring charging stations in a given area, a load scheduling algorithm is used. The optimal route planner for the electric vehicles reaching the charging station is identified and then the power carried by each feeder is calculated by cumulative power of all the charging stations. The identification of the possible route is performed by the spatial network analysis which will be executing at remote analyzer. The location, state of charge, and other details of the electric vehicle through telemetry is used to find the best charging station for the particular vehicle in view of the cost, distance and the time. The performance of the technique is evaluated with and without optimization by considering the logical constraints; and the results are presented. Springer Nature Switzerland AG 2020. -
Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT
Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized. The Author(s) under exclusive licence to Biomedical Engineering Society 2023. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Impact of user-generated content on purchase intention for fashion products: A study on women consumers in Bangalore
The advent of online media has been instrumental in providing consumers with quick, relevant, and convenient information on products and services. The success of such media has been established for businesses such as tourism, automobile, and consumer electronics- wherein consumers tend to decide on final purchases based on user - generated content (UGC) such as customer reviews and feedback rather than on traditional advertising media. With short lead times, quick turnaround of products, and frequent changes in offerings, the fashion industry is also exploring the use of such user-generated content for marketing its products. This study sought to explore and understand the relevant factors that draw consumers towards the usage of user-generated content (UGC) in the online space for the fashion business, and its impact on the purchase intention for different categories of fashion products. The study focused on the cosmopolitan city of Bengaluru, known for its fashion centricity and brand awareness. It attempted to analyze the factors for reference to media content generated by co-consumers, especially amongst women, and inferred that content that provides them with gratifications relating to social acceptance are more liable to positively influence their intent to purchase. It also specifically identified product categories that are liable to benefit from such content. -
Effective proactive routing protocol using smart nodes system
Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires. 2022 The Authors -
Vitaware-culs 2020 vitamin awareness kit /
Patent Number: 202041002544, Applicant: Erumalla Venkatanagaraju. Rapid urbanization and increase in population have evoked tremendous attention for biofuels production to combat shortage of fuels, environmental concerns, foreign exchange savings and socioeconomic issues. In recent years bioethanol production from agro-industrial wastes acquired a prominent place to fulfil the gap between production and demand. -
Process for generation of bioethanol from mannuronic acid and guluronic acid /
Patent Number: 202041002544, Applicant: Erumalla Venkatanagaraju.
Rapid urbanization and increase in population have evoked tremendous attention for biofuels production to combat shortage of fuels, environmental concerns, foreign exchange savings and socioeconomic issues. In recent years bioethanol production from agro-industrial wastes acquired a prominent place to fulfil the gap between production and demand. -
Bioparametric Investigation of Mutant Bacillus subtilis MTCC 2414 Extracellular Laccase Production under Solid State Fermentation
This work has been undertaken to investigate the bio parameters such as various substrates, initial moisture level, inoculum size, pH, incubation temperature, incubation period, metal ions and nitrogen sources effect on the production of laccase in solid-state fermentation using mutant Bacillus subtilis MTCC 2414. The laccase production was observed with a sesame oil cake (183.32 0.29 U/g), initial moisture level 80% (189.28 0.52 U/ g), inoculum size 1.5% (196.12 0.26 U/g), initial pH 8 (215.20 0.48 U/g), incubation temperature 37C (225.80 0.52 U/g), incubation period 48h (258.80 0.29 U/g), CuSO4 (263.16 0.12 U/g) and yeast extract (268.14 0.16 U/g) in the production medium. 2018, Association of Biotechnology and Pharmacy. All rights reserved. -
A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning
The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50. 2022 Tech Science Press. All rights reserved. -
Cross-layer hidden Markov analysis for intrusion detection
Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment. Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. The hidden Markov model is used to maintain the data transition and distributions in the network. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. However, the main security issues in this article are addressed using the storage and search optimization approach. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput. 2022 Tech Science Press. All rights reserved. -
Deep Belief Neural Network for 5G Diabetes Monitoring in Big Data on Edge IoT
The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. -
Physical Co-location: an intersection of problem-solving and vicarious learning
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the different forms of learning' that emerged from it. The research setting for this study involved two coworking spaces in Bangalore, India, whose constituents were co-located start-ups and established enterprises. Held from January to March 2020, the study involved conducting exploratory, semi-structured interviews with twelve firms. The findings suggested that in a co-located environment, a) firms learnt vicariously' from a rich, external knowledge base during the enquiry-led Alpha phase b) firms learnt experientially', through learning by doing and reflecting in the implementation-focussed Beta phase c) firms learnt through the process of emergence that resulted from personal reflection and team interaction, in the revelatory Gamma phase. This study lends a novel direction in acknowledging that vicarious learning, that is, learning through the experience of others, serves as a starting point for problem-solving in a co-located context. We demonstrate that firms gain familiarity with the problem through vicarious sources, that is, from those experienced co-located firms who had journeyed on a similar path. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Value Addition for Technology Start-Ups Through Physical Co-Location
Numerous economic theories, knowledge, social, and communication theories have extensively explored the phenomenon of physical co-location in various contexts. However, limited scholarly attention has been given to co-location in emerging contexts such as co-working spaces, predominantly used by start-ups. One of the critical questions examined is how co-location adds value to technology start-ups in the early and growth stages of their development. We chose a premium coworking space in Bangalore, Indias start-up capital, as the studys research setting during January March 2020. The qualitative research employed semi-structured interviews to explore the phenomenon. Our findings revealed that start-ups actively used co-located resources to explore, experiment, and validate new business ideas in the early stage. As they transitioned into the growth phase, they exploited co-located industry networks to expand into new markets. They also learned vicariously from other co-located resources and used them to solve complex problems and refined their processes and routines. As start-ups begin to grow and expand, co-location infrastructure-related costs are not justifiable, operations are less secure, and the meta culture of the co-located environment is in conflict with the firms operating culture. The results of this study have the potential to be significant for technology start-ups that are exploring new ways of working and addressing uncertainties during the early and growth stages of their development. 2021, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Educational technology at pivotal crossroads
Educational technology startups, commonly referred to as EdTech, combine education and innovative technology to transform school environments and improve student learning outcomes. Set against the backdrop of primary and secondary schools, this exploratory study uncovers the most important factors affecting the growth of EdTech startups in Bengaluru, India. Drawing on Isenberg's Entrepreneurship Ecosystem Model (2010, 2011) this exploratory, qualitative study concludes that "lack of conducive culture, infrastructure support, and finance as well as inadequacies in entrepreneurial approach and value addition" affect the growth of startups in EdTech Entrepreneurial landscape. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification
Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. -
A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN
Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested methods purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs. 2022, Tech Science Press. All rights reserved. -
Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII. -
Solvent free microwave assisted synthesis and evaluation of potent antimicrobial activity of 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido [4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles
Microwave assisted condensation of urea, thiourea and hydrazine hydrate with 1-chloro-2-formyl carbazoles in the presence of PTSA as catalyst yields 1,11H-pyrimido[4,5-a]carbazol-2-ones, 1,11H-pyrimido[4,5-a]carbazol-2-thiones and pyrazolo[3,4-a]carbazoles, respectively. The structures of the synthesized compounds have been confirmed on the basis of elemental analysis and spectral data. All the synthesized compounds have been evaluated for their antibacterial and antifungal activities. Some of the synthesized compounds 2a-g and 3a-g exhibit significant antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compounds 2a-g and 3a-g exhibit good antifungal activity against Candida albicans, Aspergillus flavus. Pyrazolo[3,4-a]carbazoles 4a-g register good antibacterial activity against Escherichia coli and Pseudomonas aeruginosa. The compound 4e indicate maximum activity of 20 and 24 mm at 500 and 1000?g/disc, respectively, against Lipomyces lopofera fungi. -
6-Bromo-2-(3-phenylallylidene)-2,3,4,9-tetrahydro-1H-carbazol-1-one
molecules of the title compound, C21H16BrNO, are linked through pairs of N-H?O intermolecular hydrogen bonds into centrosymmetric R2 2(10) dimers. One of the C atoms of the cyclohex-2-enone ring is disordered with refined occupancies of 0.61 (2) and 0.39 (2).