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Showing papers by "KCG College of Technology published in 2020"


Journal ArticleDOI
01 Nov 2020-Silicon
TL;DR: In this article, the effect of adding silicon coupling grafted ferric oxide and E-glass fiber in thermal stability, wear and fatigue behavior of epoxy resin hybrid composite was investigated, where surface grafting was done using 3-Aminopropyletrimethoxylane via aqueous solution method with acetic acid as pH adjuster.
Abstract: In this present study the effect of adding silicon coupling grafted ferric oxide and E-glass fibre in thermal stability, wear and fatigue behaviour of epoxy resin hybrid composite was investigated. The principal aim of this research was explicating the importance of silicon coupling grafted E-glass fibre and ferric oxide particle in thermal stability, wear and fatigue properties of epoxy hybrid composite. Ferric oxide particles of 800, 200 and < 100 nm and E-glass fibre of 600 GSM was used as second phase additions in epoxy resin with surface grafted condition. The surface grafting was done using 3-Aminopropyletrimethoxylane via aqueous solution method with acetic acid as pH adjuster. The improvement of 80% was observed in initial thermal stability of surface grafted E-glass fibre epoxy composite on comparing with un-modified glass-epoxy composite. Similar improvements were noted in rapid and final decomposition stages also. The lower specific wear rate of 0.002 was observed for surface grafted E-glass and ferric oxide added composite designation EGFI11. The worn surface frcatograph explicated flat and smooth wear track surface for surface grafted composite designations. A highest fatigue life cycle of 18,724 is observed for surface modified composite designation EGFI21. These thermally stable and high wear resistance and fatigue strengthened composites could be used in automobile, aircrafts and domestic applications.

97 citations


Journal ArticleDOI
TL;DR: In this article, a single cylinder, diesel engine with nano-emulsion of orange peel oil biodiesel was evaluated and its performance was compared with pure OOME fuel at peak load condition.

64 citations


Journal ArticleDOI
01 Sep 2020-Optik
TL;DR: CdO-CuO nanocomposite was synthesized by a microwave-assisted process and examined by employing different spectroscopic methods as mentioned in this paper, the cubic and monoclinic crystal structure of CdO and CuO, respectively, with crystallite size around 25 nm and spherical shape were confirmed by XRD and SEM with EDAX analysis.

51 citations


Journal ArticleDOI
TL;DR: In this paper, an optimization of the carrier density of the TiO2 nanotube electrodes for supercapacitor applications is reported, which achieves an ultra high carrier density with a capacity of 2.73 × 1022 cm−3 by controlling the fabrication conditions.

24 citations


Journal ArticleDOI
TL;DR: In this paper, the authors investigated the indentation induced damage resistance in glass fiber reinforced polymer composite laminates under normal and inclined planes, and showed that the damage resistance of the laminated composite can be improved by using indentation-induced damage resistance.
Abstract: This work relates to the investigation of indentation induced damage resistance in glass fiber reinforced polymer composite laminates under normal and inclined planes. Uni-directional [0] and cross...

21 citations


Journal ArticleDOI
TL;DR: The objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully and it was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset.
Abstract: Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully.

18 citations


Journal ArticleDOI
01 Dec 2020
TL;DR: The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction and utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification.
Abstract: Numerous public networks, namely Instagram, YouTube, Facebook, Twitter, etc., share their own feelings and idea as videotapes, posts, and pictures. In future research, adapting to such data and mining valuable information from it will be an undeniably troublesome errand. This paper proposes a novel audio–video–textual-based multimodal sentiment analysis approach. The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction. A feature-level fusion technique is employed in fusing the extracted features from different modalities. Therefore, the extracted features are optimally chosen by using a novel oppositional grass bee optimization (OGBEE) algorithm to obtain the best optimal feature set. Here, 12 benchmark functions are developed to validate the numerical efficiency and the effectiveness of a novel OGBEE algorithm for various aspects. Moreover, our proposed approach utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification. The experimental analysis reveals that the proposed approach provides better classification accuracy of about 95.2% with less computational time.

16 citations


Journal ArticleDOI
11 Feb 2020
TL;DR: The trust stage computation of range and KNN query answers is exposed with the help of the whale optimization algorithm (WOA) and the effectiveness of the proposed concept is evaluated through various consequences.
Abstract: Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.

15 citations


Journal ArticleDOI
TL;DR: In this paper, the authors reported the synthesis of graphene oxide and polyvinyl poly pyrrolidone composite and its application in the extraction of aflatoxins in food samples.
Abstract: The present investigation reports the synthesis of graphene oxide–polyvinyl poly pyrrolidone composite and its application in the extraction of aflatoxins in food samples. This composite was successfully synthesized and characterized with analytical instruments such as UV–Vis, FITR, XRD, and SEM techniques. The extraction was carried out by reinforced hollow fiber liquid-phase microextraction coupled with high-performance liquid chromatography (HPLC). Various parameters that affect the productivity of the present technique were exhaustively explored, and the quantifications were carried out under the optimized states. The limits of detection (LODs) were found to be 0.33, 0.10, 0.37, and 0.10 ng g−1, and limits of quantification (LOQs) were 1.10, 0.33, 1.24, and 0.33 ng g−1, respectively, for aflatoxins B1, B2, G1, and G2. The accuracy of the present method was determined based on the relative recovery (RR%) of the aflatoxins and the RR% of aflatoxins B1, B2, G1, and G2 which were found to be quite good (between 60.20 and 108.20 for 1 ng g−1 and between 62.02 and 95.39 for 5 ng g−1) in food samples. Applicability of the sorbent for the separation and quantification of the above-mentioned aflatoxins from food samples were examined. The developed method is straightforward, reliable, and effective, and has been successfully applied in determination of aflatoxins from food samples.

14 citations


Book ChapterDOI
01 Jan 2020
TL;DR: The goal of this project is to find whether a given medicine is fake or original using blockchain technology, an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but also has the potential to disrupt other markets.
Abstract: The goal of this project is to find whether a given medicine is fake or original using blockchain technology. Blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but also has the potential to disrupt other markets. It removes the need for trusted intermediaries, can facilitate faster transactions and add more transparency. A medical product is counterfeit when there is false representation in relation to its identity or source. In the case of medicines, the implications are more severe. This technology stops the entry of fake drugs into the supply chain, mainly the part between the manufacturer and consumer. The technology uses digital signature where each block gets a unique crypto id. This digital signature is provided for each block and it gives a strong control of ownership.

12 citations



Journal ArticleDOI
TL;DR: This work has developed a noninvasive method for identifying the anaemic status of a person by estimating their haemoglobin (Hb) level that’s based on the Ridge Regression algorithm.
Abstract: Anaemia is one of the most prevalent nutritional deficiency disorders in the world that affects 1.62 billion people of all age groups. Its effects are more significant in the preschool-age children and pregnant women. The objective of this work is to develop a noninvasive method for identifying the anaemic status of a person by estimating their haemoglobin (Hb) level. Data collected from 135 participants is used for developing this model in which 80% of them (108 participants) were classified as a training group and the rest (27 participants) were grouped into a test group and their data is used for testing the model that’s based on the Ridge Regression algorithm. Haemoglobin level of a person is predicted from their digital image of the lower palpebral conjunctiva and basic details like age, sex, height, weight and BMI. These predicted values are closer to the values measured by the standard invasive methods and the Pearson correlation coefficients between the measured haemoglobin value and the predicted haemoglobin value are 0.722 and 0.705 for training and testing respectively. This model is trained to perform well with any smartphone in almost any non-ideal lighting condition without the usage of any external hardware. A web application and mobile application both called 'Chromanalysis' were developed and made available to anyone for screening their anaemic condition with their conjunctiva image and other basic details. This application will help users to assess their haemoglobin levels frequently without undergoing invasive procedures.

Journal ArticleDOI
TL;DR: A fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs is proposed to resolve the conflict of privacy preservation and it is proved that the HCP DS based proposed approach can efficiently meet the requirements of security and privacy in VANSETs.
Abstract: Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

Journal ArticleDOI
TL;DR: In this article, the reported low cost activated carbon as an adsorbent material and their SEM analysis is performed. And the adsorption properties of the activated carbon materials based on their sources for the further experimental investigations are also reviewed.

Journal ArticleDOI
TL;DR: HCNC became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks and defines a new similarity measure based on the degree of a node and it’s adjacent nodes degree.
Abstract: Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.

Journal ArticleDOI
TL;DR: In this article, the authors proposed continuous authentication (CA) process using multimodal biometric traits considering finger and iris print images to various feature extraction process, and the final feature vector is acquired by concatenating directional information and center area features.
Abstract: The biometric process demonstrates the authenticity or approval of an individual in view of his/her physiological or behavioural characteristics. Subsequently, for higher security feature, the blend of at least two or more multimodal biometrics (multiple modalities) is requiring. Multimodal biometric technology gives potential solutions for continuous user-to-device authentication in high security. This research paper proposed continuous authentication (CA) process using multimodal biometric traits considers finger and iris print images to various feature extraction process. At that point, features are extracted into optimal feature level fusion (FLF) process. The final feature vector is acquired by concatenating directional information and centre area features. Disregard the optimal feature process the inspired fruit fly optimisation (FFO) model is considered, and then these model is fused into authentication procedure to find the matching score values (Euclidian distance) with imposter and genuine user. From the approach, results are accomplished most extreme accuracy, sensitivity and specificity compared with existing papers with better FPR and FRR value for the authentication process. The result shows 92.23% accuracy for the proposed model when compared to GA, PSO which is attained in MATLAB programming software.

Journal ArticleDOI
TL;DR: A new set of delay dependent sufficient conditions is derived in terms of linear matrix inequalities, which guarantees that all agents asymptotically converge to the convex hull with the prescribed H∞ and passive performance.
Abstract: This paper studies the consensus problem of a second-order nonlinear multi-agent system with directed topologies. A distributed control protocol is proposed for each agent using the relative states among neighboring agents. A mixed H∞ and passivity-based control is maneuvered to deal the bounded disturbances enduring in the system. Based on the theory of the sampled-data control technique and Lyapunov stability theory, some novel conditions are given to realize the consensus of a class of second-order multi-agent nonlinear systems. A new set of delay dependent sufficient conditions is derived in terms of linear matrix inequalities, which guarantees that all agents asymptotically converge to the convex hull with the prescribed H∞ and passive performance. Finally, an example with simulation results is given to verify the theoretical results.

Journal ArticleDOI
TL;DR: The simulation results proved the efficiency of the proposed trust based framework on detecting dishonest nodes, the malicious data transmitted by the honest/dishonest nodes and also detecting the colluding attacks that destroys the network resources in a short period.
Abstract: This paper proposes a new Trust based DDoS Attack Removal Framework (T-DARF) for effective trust management in VANETs. Based on a newly developed data centric validation unit and intrusion detection unit, all the misbehaving nodes are removed and allows the framework to prevent DDoS attacks in a distributed and collaborative manner. In other words, our proposed framework ensures a trusted, reliable communication between vehicles and delivering reduced network overhead by handling the DDoS attacks. T-DARF is built of three elements: (1) for the detection of dishonest nodes it includes a collaborative and distributed elements, (2) to filter the malicious data it includes a data-centric validation element, and (3) a delay checking element for the detection and prevention against DoS and DDoS attacks. Also, a new trust-based routing protocol is developed that uses the idea of companions (i.e. integrating the notions of link quality and trust value of neighbors) to select the trusted vehicles and to select the optimal path we imposed a hybrid Dragonfly based particle swarm optimization (DPSO) algorithm. The simulation results proved the efficiency of our proposed trust based framework on detecting dishonest nodes, the malicious data transmitted by the honest/dishonest nodes and also detecting the colluding attacks that destroys the network resources in a short period. Also, T-DARF in a worst case scenario can sustain its performance degrees and outperforms the effectiveness of existing schemes such as TRIP, AECFV and T-CLAIDS.

Journal ArticleDOI
TL;DR: In this article, the authors used the decision function-based chaotic salp swarm (DFCSS) algorithm to select the optimal features in the feature selection process and then the chosen attributes are given to the improved Elman neural network (IENN) for data classification.
Abstract: Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre-processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function-based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS-IENN-based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS-IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high-classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K-nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.

Journal ArticleDOI
01 Dec 2020
TL;DR: This proposed APSO-MVS algorithm has considered multiple node metrics (node distance from the cluster group centre, node speed and node density) for the selection of an optimal leader node and proved the efficacy of proposed protocol in overhead reduction compared to other existing auto-configuration protocols.
Abstract: In this paper, we propose a hierarchical topological-based auto-configuration scheme for MANETs providing global internet connectivity among leader and member nodes to reduce the control overhead. The proposed scheme has performed the duplication address detection (DAD) operation through selecting a pre-configured node called coordinator node by a new joining cluster node. Hence, the overhead is reduced by the elimination of DAD messages broadcasting in the whole network. Also, the clustering problem in MANETs is solved by introducing a new adaptive particle swarm optimization with multiple velocity strategy (APSO-MVS) algorithm for a new leader selection with the frequent departure and failure of a leader node. However, to enhance the robustness and global searching ability of classical PSO, the three new velocity updating strategies are used in a newly developed APSO-MVS algorithm. This proposed APSO-MVS algorithm has considered multiple node metrics (node distance from the cluster group centre, node speed and node density) for the selection of an optimal leader node. Simulation results have proved the efficacy of proposed protocol in overhead reduction compared to other existing auto-configuration protocols and in terms of 15 benchmark test functions.

Journal ArticleDOI
TL;DR: In this paper, two types of engine modifications were made: Low Heat Rejection (LHR) and Low Temperature Combustion (LTC), and the engine parts such as the cylinder head and the piston head were coated by the alumina (Al2O3) with a thickness of 350μm.

Journal ArticleDOI
TL;DR: In this article, the size and the stability of the Schiff base modified AgNPs were explored using DLS and Zeta potential analysis, and the results of the present study confirm that SB-cinnamaldehyde-stabilized AgNs have significant potential as an antimicrobial agent in treating infectious diseases.
Abstract: In the current work, silver nanoparticles (AgNPs) were green synthesized using starch and modified with anionic dye (acid fuchsin) and aldehyde (salicylaldehyde and cinnamaldehyde). Thus in situ formation of Schiff base stabilized AgNPs were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM) and transmission electron microscope (TEM) analysis. The size and the stability of the Schiff base modified AgNPs were explored using DLS and Zeta potential analysis. TEM analyses showed the size of the Schiff base (cinnamaldehyde) modified AgNPs with an average particle size of 5–10 nm. The synthesized Schiff base modified AgNPs were tested for their antibacterial efficacy against gram-negative bacteria such as Escherichia coli (MTCC733), Pseudomonas aeruginosa (MTCC1688) and gram-positive bacteria such as Bacillus subtilis (MTCC41) and Enterococcus faecalis (MTCC96). The antifungal activity of the synthesized materials has been studied against Candida albicans along with standard fluconazole. Higher antibacterial and antifungal activities were observed for the Schiff base (SB) capped AgNPs due to the combined effect of SB and AgNPs. The mechanism of the bactericidal activity of nanoparticles is suggested as due to the interaction of AgNPs with the cell membrane. The results of the present study confirm that SB stabilized AgNPs have significant potential as an antimicrobial agent in treating infectious diseases.

Book ChapterDOI
01 Jan 2020
TL;DR: In order to increase security and confidentiality, author generates the new group key via Email using Diffie-Hellman algorithm using email address generator in case of a new user is added or an existing user leaves themselves from the group.
Abstract: Cloud computing is the recent technology used to share and store the computer resources rather than having resources in local server to maintain the application. Though cloud is used for a large amount of storage, there is no security in cloud. In general, all the groups have data owners and data members each should have user name, key, and group key. If a user shifts from one group to another, they can easily access the information from another group. It leads to a security problem. In order to increase security and confidentiality, author generates the new group key via Email using Diffie-Hellman algorithm. In case of a new user is added or an existing user leaves themselves from the group. The data members have to get permission from the data owners in case of any data updation. If the user misbehaves, i.e., (DDOS) attack, data owner or cloud terminates the user from the group. The updated key is sent to the users through Email. This mechanism significantly improves security in cloud computing.

Journal ArticleDOI
TL;DR: In this paper, a multi-layer thin-film 2D material which consists of CNTs as-synthesized thin film as the base material is synthesized.

Book ChapterDOI
01 Jan 2020
TL;DR: A number of the foremost common strategies together with various algorithms and computational simulation strategies are reviewed within the field of various resolution strategies for energy storage systems and dynamic programming strategies are found within the literature.
Abstract: The optimum operation and amount of energy storage are operated by a buyer who faces unstable electricity costs and seeks to decrease its energy prices. The worth of storage is demarcated the consumer’s Internet profit obtained by optimally operative the storage. Model projecting management based mostly coordinated planning framework for different renewable energy generation then battery energy storing arrangements is accessible. On the idea of the short forecast of accessible renewable energy generation and cost info, a joint look-ahead optimization is performed by completely the various power plants and storage system to work out their internet energy booster towards the electrical network. In concurrence with moderate battery capability, the surplus unpredictable renewable power generation may be charging the battery storage and contrariwise. This paper presents an outline; in addition, overall educations of analysis and development within the field of various resolution strategies for energy storage systems and dynamic programming strategies are found within the literature. This paper has reviewed a number of the foremost common strategies together with various algorithms and computational simulation strategies. This paper provides help for the upcoming studies for those interested in the problem or proposing to do additional research in this area.

Journal ArticleDOI
TL;DR: Different approaches such as GA-based and PSO based algorithms are surveyed and analyzed for preserving the privacy of data and the purpose of data sanitization and the use of Bio-Inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm are discussed.
Abstract: Security of the data is also concerned with the privacy of the data since the data or the information can be easily disclosed. Data sharing also plays a key role in security. Recently, patterns are disclosed using associative rule mining and the sensitive information are one of the imposing threats to the security aspects in data mining. Preserving the data as well as the privacy of the user using several PPDM approaches leads to provide authorized access for such sensitive information. The security threats for preserving privacy are provided by developing a sanitization process. The sanitization process is considered to be one of the biggest challenges in the mining of data. In this paper, different approaches such as GA-based and PSO based algorithms are surveyed and analyzed for preserving the privacy of data. The purpose of data sanitization and the use of Bio-Inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are discussed.

Journal ArticleDOI
TL;DR: In this paper, an attempt has been made to investigate the influence of cutting speed and feed rate on various machining aspects like cutting forces, chip morphology, surface roughness and tool wear during the orthogonal turning of Inconel 718.
Abstract: Inconel 718 is a nickel-based super alloy well suited for high-temperature applications encountered in space shuttles, aircraft black box and turbocharger due to their inherent properties. Taking into account of extreme working conditions, efficiency in the process of machining without affecting the nature of the surface integrity with utmost care assumes a lot of importance. In this current study, an attempt has been made to investigate the influence of cutting speed and feed rate on various machining aspects like cutting forces, chip morphology, surface roughness and tool wear during the orthogonal turning of Inconel 718. Also, the work has been focused on feed forces and thrust forces to understand the proper material deformation behaviour and surface integrity.

Journal ArticleDOI
01 Jun 2020
TL;DR: In this paper, the authors propose to retrain or replace a few units in a subcritical facility to improve overall efficiency of conversion of energy in a power plant but also support sustainability issues.
Abstract: Retrofit or replacement of few units in a subcritical facility may not only improve overall efficiency of conversion of energy in a power plant but also support sustainability issues. The primary o...

Journal ArticleDOI
TL;DR: The result shows that the dengue outbreak in Tamil Nadu during 2017 was due to the population, water stagnation, and sewage, whereas the human activity weren’t the cause of the d Dengue outbreak which caused 65 deaths.
Abstract: Dengue has been indigenous to India in last decade. There was a major outbreak in the state of Tamil Nadu in 2017. Here, we investigate the dengue outbreak in parts of Tamil Nadu, India. Dengue case data were obtained from the hospital records in the Chennai district of Tamil Nadu. The data were analyzed using statistical approaches such as correlation and regression. The result shows that the dengue outbreak in Tamil Nadu during 2017 was due to the population, water stagnation, and sewage, whereas the human activity weren’t the cause of the dengue outbreak which caused 65 deaths. Male constitutes 54.71% whereas female accounted for 45.29% of dengue incidence in Tamil Nadu, majority deaths were children aged less than 10 years due to the outbreak of Dengue Hemorrhagic Fever (DHF). This investigation was evaluated using mathematical regressions, Geographically Weighted Regression (GWR) regression outperformed Ordinary Least Square (OLS) regression model in detecting dengue incidence. This investigation can be strengthened by implementing a surveillance system in parts of Tamil Nadu before an outbreak.

Book ChapterDOI
06 May 2020
TL;DR: The hybrid encryption technique is combined with an LDPC coder for efficient information between the sender and receiver and improves the hiding capacity by 11.36% than SISO, 48.48% than OFDM, and 68.96% than MIMO.
Abstract: Medical identity theft is more common since it is easy to steal the patient’s record details and sell it for illegal medical trades. It happens in medical track records, personal healthcare accounts, recorded online insurance services, social security numbers, and other personally identifiable information (PII) which are disguised and used in the name of others. In this research, the proposed approach combines data hiding with hybrid encryption algorithms such as Modified Elliptic Curve Cryptography (MECC) encryption based on MIMO-OFDM framework. The hybrid encryption technique is combined with an LDPC coder for efficient information between the sender and receiver. Thereby, it improves the hiding capacity by 11.36% than SISO, 48.48% than OFDM, and 68.96% than MIMO. The sensitivity values are measured through the parameter number of pixels changes rate (NPCR) and unified average changing intensity (UACI) and obtained as 99.70 and 33.48 respectively. Correlation between adjacent pixels (CP) is increased compared to previous works. For SNR (Signal to Noise Ratio) at 30 dB CP is obtained as 0.9985. Simulation is carried out in MATLAB 2019a.