bluuebunny/crossref_metadata_2025 huggingface dataset:
Preparation of single-cell suspensions for scMEP mass cytometry analysis v1
Authors: J Hartmann Felix
Published: March 2021

Preparation of single cell suspensions to be analyzed for metabolic and phenotypic features by scMEP mass cytometry analysis. This protocols spans 1) optional incubation of cells with small molecules to be able to asses biosynthesis rates of DNA, RNA and protein 2) cisplatin-based live/dead staining 3) PFA-based cell fixation and 4) cryopreservation.
Inducible Clindamycin Resistance in Staphylococcus aureus isolated from pus samples in an Orthopaedic tertiary care centre.
Authors:   Assistant Professor, Department of Microbiology, Sanjay Gandhi Institute of Trauma and Orthopedics, Bangalore, Karnataka, India, S Dr. Pratibha , Kumar Dr. Praveen ,   Senior Technician, Department of Microbiology, Sanjay Gandhi Institute of Trauma and Orthopedics, Bangalore, Karnataka, India
Published: March 2021

Introduction: Clindamycin is a commonly used antibiotic to treat skin and soft tissue infectionscaused by Staphylococcus aureus particularly Methicillin-Resistant Staphylococcus aureus (MRSA)infection. In vitro routine tests for clindamycin susceptibility may fail to detect inducible clindamycinresistance due to genes resulting in treatment failure, thus necessitating the need to detect suchresistance by a simple D - test on a routine basis. Materials and Methods: 165 isolates ofStaphylococcus aureus were subjected to routine antibiotic susceptibility testing including Oxacillin(1μg) and Cefoxitin (30μg) by Kirby Bauer disc diffusion method. Inducible clindamycin resistancewas detected by D test as per CLSI guidelines on erythromycin resistant isolates. Results: 24(14.5%) isolates showed inducible clindamycin resistance, 8 (4.84%) showed constitutive resistancewhile the remaining 59 (35.75%) showed MS phenotype. Inducible clindamycin resistance and MSphenotype were found higher in MRSA (21.42%, 40.47%) as compared to MSSA (7.40%, 30.86%).Conclusion: This study showed that the D test should be used as a mandatory method in routinedisc diffusion testing to detect inducible clindamycin resistance.
Patterns of pre-donation deferral in voluntary blood donors with its impact on the donor pool- A study in a tertiary care hospital
Authors:   Assistant Professor, Department of Pathology, PES Institute of Medical Science and Research, Kuppam, Andhra Pradesh, India, Anegundi Dr. Reshma , B N Dr. Divyashree ,   Assistant Professor, Department of Pathology, PES Institute of Medical Science and Research, Kuppam, Andhra Pradesh, India
Published: March 2021

Background: The prime responsibility of a Blood Transfusion Service is to provide a very safe,sufficient and timely supply of blood and its components to those in need. In fulfilling thisresponsibility, the Blood Transfusion Service should ensure that the act of blood donation is safe andcauses no harm to the donor. Aims and objectives: To estimate the incidence of pre-donationdeferral in a tertiary care institute and to estimate various factors for donor deferral. Material andmethodology: A retrospective analysis of donors data was collected from January 2019 toDecember 2019 from the donor deferral registry maintained in the blood bank. In this study, thesubjects who visited our blood bank or arrived at a voluntary blood donation camp for purpose ofdonating blood were screened by donor questionnaire and basic physical examination done withrecording vital signs(temperature, blood pressure, pulse rate) followed by hemoglobin estimationand blood grouping. Results: In the present study, 456 donors were deferred from donation. Thedeferral rate was 13.6%. Deferral rate was high among males. The majority of deferrals, weretemporary deferrals, 380(83.33%) and 76(16.66%) were permanent deferrals. Anemia was themost common cause among temporary deferrals constituting 56.05% of temporary deferrals and46.71% of total deferrals. Conclusion: Attempts should be made to create awareness amongpotential donors by conducting health talks, distributing educational pamphlets, counselling donorsduring donation and encouraging them for future donations.
DEFECTED GROUND COPLANAR MULTI-BAND-PASS MICROSTRIP FILTER
Authors: Rakesh Kumar Maurya, Pradyot Kala Reena Pant,
Published: March 2021

This paper presents a combination of an inductive coupling technique and coplanar ground plane microstrip filter. Here, inductive coupling technique is used to overcome the unwanted radiation loss generated by the gap between the co-planar ground plane and the transmission line which improves filter characteristics. A defected ground structure (DGS) is integrated with the proposed filter to achieve a tri-bandpass characteristic (1.85, 3.53, and 5 GHz) without hampering the filter performance. The experimental results of the proposed filter are found in good agreement with simulated results.
AMELIORATION OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN MEDICAL MANAGEMENT (A CASE STUDY ON THE PANDEMIC OF COVID 19 CASES IN ASIA)
Authors: Felcy Judith Sagaya Aurelia,
Published: March 2021

Machine learning is seeing increasingly utilized in medical management for different reasons: tremendous sums of information are being captured and made accessible carefully; handling of vast sums of information has gotten to be cost-effective due to the expanded computing control presently accessible at reasonable costs; and different open source systems, toolkits, and libraries are accessible that can be utilized to construct and execute ML applications. Particularly in healthcare, ML has driven to energizing modern improvements that may rethink COVID-19 treatment and vaccination within a long time to come. With modern occurrences of the brand modern coronavirus clutter, COVID-19, creating day through the day, it is common to compare the unused affliction to other flare-ups in current history. Machine learning can offer assistance to anticipate three sorts of restorative dangers - disease, seriousness, and result. Whereas it is still it is early for COVID-19 with machine learning, but early applications seem promising.  Machine learning is utilized when a computer has been instructed to recognize designs by giving it with information and a calculation to assist get it that information. This method of learning from the information is called training and the yield that we accomplish is through testing. Machine learning-based robotic surgery is changing the way surgery is performed nowadays. Machine learning in healthcare is getting to be more broadly utilized and is making a difference in patients and clinicians in numerous diverse ways. Cybersecurity and protection are major concerns and open challenges in healthcare which are yet to be addressed.
AN ASSESSMENT - WATER QUALITY MONITORING PRACTICES AND SEWER ROBOTIC SYSTEMS
Authors: Rupali S. Kokate Saniya Ansari, S. M. Khairnar, Ravindra R. Patil,
Published: March 2021

Water pollution has become a global issue and creating a severe problems due to contamination of various water sources such as agricultural waste, industrial waste, and sewage waste etc. So, the available sensory systems, nodes, and techniques plays a crucial role in identification of harmful pollutants in distinct water resources. The water pollutants monitoring techniques with prospered tools and sensors have been unfolded to find gap for future research maneuver. The need of real-time newer sensory system has been put forwarded for identification of mostly occurring harmful pollutants such as BOD, COD, TSS, and Hydrogen sulfide in examination of water quality.

In this modern advancement, robotic systems have taken an intrinsic stand in diverse fields of humankind. An underground infrastructure is a base of modern society. In this paper, the existing methodologies and developed sewer robotic systems have been discussed and concluded on their applications, limitations and impact on realistic scenarios. Also, the cardinal point is revealed that previous art work focused only on sewer defect identification but not standardized work on sewer blockages detection and removal. So, the sewer robotic system with features of cost effectiveness and standardized accuracy matrix should be developed to resolve sewer blockage issues and followed by human scavenging. The survey outcomes extend a province of sewer robotics to resolve blockages issues of buried sewers of distinct diameters in real-time with substantial methodology.
CLUSTER BASED ROUTING PROTOCOLS IN WSN SENSOR
Authors: Dr. Abhishek Badholia Kanak Prabha Lila Ramani,
Published: March 2021

Over the past few years we can observe the WSNs or Wireless Sensor Network applications in various fields increasing immensely. The energy efficiency, network lifetime and clustering process prime goal is the working network's optimization is the focus of many of the routing algorithm. Keeping in mind the network homogeneity, for network performance reinforcement we suggest instead of single path to use multiple paths. For WSNs, Reinforcement Intelligence Routing Protocol (RIRP)[1]. In the multihop wireless sensor networks an efficient and effective method for security improvements local monitoring has worked well [2]. But taking in consideration the power consumption in the current practice of local monitoring is costly. For ensuring long-lived operations in the sensor network reinforcement intelligent routing protocol is critical [3]. For ensuring both the aspects improvement in security and long-lived operations, the development of mechanism that is effective and incorporated with the Reinforcement protocol is an open problem. With the help of local monitoring to solve this issue, section of the traffic going in and out of its neighbors is supervised by each node to keep a check on any suspicious behavior like unlikely long delays in packet forwarding [4]. To integrate the existing reinforcement protocol of the network and without any niggardly in the consumption of energy in the local monitoring with the help of a protocol [5]. In comparison to other protocols in this protocol the region of instability starts later. At a constant rate the nodes of the RIRP or Reinforcement Intelligent Routing Protocol dies. Few problems such as cluster head selection process, network lifetime and network stability are evaluated and worked in the technique proposed here [6]. To reduce the overload consumption as much as possible the nodes switches in between the active and sleep mode.
REAL TIME FACE DETECTION AND RECOGNITION SYSTEM USING HAAR CASCADE CLASSIFIER AND NEURAL NETWORKS
Authors: Shilpa Sharma Anirban Chakraborty,
Published: March 2021

Home protection and privacy have become one of the most critical aspects in today's world. As technology progresses at an exponential pace, the times are not far ahead for each house to be fitted with sophisticated security systems to deal with regular burglary and theft. But as one side of the tech progresses, so do its detrimental counterparts. DES encryption can be an indicator of how easily an encrypted piece of information can be deciphered. Not long after its release, DES encryption was referred to as 'unsafe' and with today's modern application, anything like DES might be an open invitation to hack. With many developments in the field, the technology has, in many respects, surpassed the use of biometrics (finger prints). Face recognition, nowadays, is present in almost every smart device that has some piece of information stored that holds importance to its users. With facial recognition gaining popularity, many tech companies have come with their own patent to make a technology related to Facial Recognition on the market. This paper suggests a somewhat related concept as to how home protection can be improved by using a face detection and recognition algorithm (Haar Cascade Classifier).
FRAME SAMPLING ASSISTED DEPTH MOTION MAPS FOR DEPTH BASED HUMAN ACTION RECOGNITION
Authors: K. Mohanram, S. Jagadeesh Sivanagi Reddy Kalli,
Published: March 2021

The discovery of depth sensors has brought new opportunities in the Human Action Research by providing depth image data. Compared to the conventional RGB image data, the depth image data has additional benefits like color, illumination invariant, and provides clues about the shape of body. Inspired with these benefits, we present a new Human Action Recognition model from depth images. For a given action video, the consideration of an entire frames constitutes less detailed information about the shape and movements of body. Hence we have proposed a new method called Frame Sampling to reduce the frame count and chooses only key frames. After key frames extraction, they are processed through Depth Motion Map for action representation followed by Support Vector Machine for classification. The developed model is evaluated on a standard public dataset captured by depth cameras. The experimental results demonstrate the superior performance compared with state-of-art methods
AES AND RSA-BASED HYBRID ALGORITHMS FOR MESSAGE ENCRYPTION & DECRYPTION
Authors: Dr Asha Ambhaikar Mr. Abhishek Guru,
Published: March 2021

File encryption is an easy means of securing personal or business data protection. The RSA and AES representative encryption algorithms are not capable of satisfying the criteria of file encryption reliability and security when used separately. A hybrid encryption algorithm mixing AES and RSA algorithms is suggested in this paper to overcome the above issues in order to solve file encryption performance and security problems. The experimental results suggest that the RSA and AES hybrid encryption algorithm can not only encrypt files, but also provide the benefits of efficiency and protection of the algorithm.
THE STUDY OF THE DISCOURSE RELATED TO THE CODE OF ROBOETHICS FOR THE HUMAN-ROBOT INTERACTION PROFESSION IN SUJOY GHOSH’S ANUKUL
Authors: Mukherjee Dr. Panchali
Published: March 2021

The research paper primarily studies the idea or discourse related to the code of “Robot Ethics” or “Roboethics” which governs human-robot interaction profession and is embodied in Sujoy Ghosh’s film narrative Anukul (2017). It foregrounds the theory of “discursive formations” that propounds the formulation of knowledge from discourses that pre-exist the subject’s experiences. The paper shows that the subject is not an autonomous or unified identity but is in process as a result there is a parallel shift in the history and philosophy of science. The paper attempts to explore the evolution of the “Robot Ethics” in the context of the film. It attempts to show that science progresses in discontinuous movement from one discursive formation or paradigm to another in connection to the development in the code of “Roboethics” as projected in the film narrative. The paper shows that the scientists conduct and write up their research within the conceptual limits of particular scientific discourses which are historically situated in relation to their society and culture. It shows that discourse related to “Robot Ethics” is connected to power.  The research paper shows that individuals are subjects of ideology and the ideology/ies operate by the interpellation of the subjects in the social structure. This interpellation works through the discursive formations which are materially linked with “state apparatuses” such as religion, law and education.
Analysis of Crop disease detection with SVM, KNN and Random forest classification
Authors: MR. SANJAY SHITOLE MRS. RUPALI KALE,
Published: March 2021

Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.
A REVIEW OF THE SECURITY ISSUES IN CLOUD COMPUTING AND ITS REMEDIAL ACTION
Authors: Sahar Altaf, Muhammad Wajahat Ali Muhammad Waqar Khan, Sumaira Yousuf Khan,
Published: March 2021

In the recent years, cloud computing has become a widely utilized revolution in the field of data modernization due to its favorable circumstances like high processing power, less expense of administrations, elite adaptability, unwavering quality and  accessibility. It is an integral tool that improves the cost of equipment, controllability and utility to share the information and so forth numerous organizations are turning their applications and administrations on the cloud. It offers secure and versatile administrations but in every case there exists some cloud security and protection issues when information has sent from a focal stockpiling worker to an alternate cloud, individual and private information augment the danger of information secrecy, respectability, accessibility, and verification before one pick a merchant in the cloud or pick the cloud and move services in the cloud. In this research, paper several articles are reviewed that deals with the security issues and the remedial actions and responses that have been taken by researchers and organizations in the field of cloud computing. This analysis provides insight to future research opportunities to students, researchers, publishers and experts and help them to study current research trend and security issues related to cloud computing.
Mediating Effect of Accounting Practices between Accounting Information System in Enterprise Resource Planning System and Firm Performance
Authors: Haleem Athambawa
Published: March 2021

This study's main objective is to examine the mediating effect of accounting practices between AIS in ERP environment and firm performance of listed companies in Sri Lanka. AIS has two main dimensions, such as ERP System Quality and Accounting information Quality(ACIQ). The total population of the study consist of 295 companies. A random sampling method was applied, and 217 companies were used as a sample for this study.  Amos 23 version was used as an instrument to analyze the data, and the structural equation model was applied to test the path relationship of the constructs.  The study shows that ERP system quality and ACIQ have a statistically significant influence with Accounting practices and firm performance, while accounting practices have a significant influence with firm performance. In addition to this, accounting practices have a significant mediating effect between AIS and firm performance.   IS managers and accounting professionals can use the finding and also should pay more attention to improve ERP System Quality and ACIQ in order to enhance Accounting Practices which subsequently having implication on firm performance.  AIS quality and organizational performance will also be enhanced when new AIS applications and capabilities are brought in, as per the resource-based view.
IMPACT OF QUEUING DISCIPLINES ON THE PERFORMANCE OF MULTI-CLASS TRAFFIC IN A NETWORK
Authors: R. Sherine Jenny N. Sugirtham,
Published: March 2021

Network applications demand quality of service for enhanced call quality and increased user satisfaction. Hence, implementing queuing discipline at switches and routers in a network will govern the way packets are buffered while waiting for transmission. Queuing disciplines like first-in first-out (FIFO) queuing, priority queuing (PQ), weighted-fair queuing (WFQ), custom queuing and modified weighted round robin are more prominently deployed in network applications. These queuing disciplines help to control and manage network resources by fixing priorities for specific types of data on the network. The paper aims to analyze the quality of service parameters for various real time applications like File transfer protocol(FTP), email, database, Voice over IP(VOIP), video and web browsing,etc., and helps to optimise the use of available network resources. Moreover the quality of the network is compared using different QoS parameters such as end-to-end delay, throughput, jitter and Mean Opinion Score (MOS). This paper focuses on various queuing disciplines with low and high network traffic.
MACROECONOMIC FACTORS, CORRUPTION, NPAS IN 4 PUBLIC SECTOR BANKS: A CROSS STUDY
Authors: Krishnaswamy Sai Preethi
Published: March 2021

Banks play a vital role in economic development and growth of a country. A sound and healthy financial institution ensures overall stability of the system. Commercial and cooperative banks together constitute the Indian Banking System. Commercial Banks account for more than 90% of the banking sector’s assets. The public sector banks account for a substantial part of the banking activity in India. The growth in banking sector has been burdened and hindered by increasing non-performing assets. In this paper, we aim to understand the influence of macroeconomic factors such as GDP per capita, Real Interest rates and inflation along with some bank factors such as bank size, diversification of assets and priority sector lending on NPAs of four PSBs - Punjab National Bank, Andhra Bank, Corporation Bank and Indian Bank. This paper also aims to understand whether corruption could be a determinant for NPAs in these banks.
SOCIAL MEDIA ANXIETY FROM THE VIEW OF JORDANIAN YOUTH: A META CONTENT ANALYSIS
Authors: Areej Al_Ahmad Anwar Bani Hani, Rohaya Talib, Evania Yafie,
Published: March 2021

Anxiety is contemplated to be one of the critical issues that Jordanian youth suffer from. In recent years, it was discovered from an array of studies that Jordanian youths (JY) have a big problem related to social media anxiety (SMA), which is regarded as the reason for excessive use of social media platforms (SMP). The highlighted issue here will be mentioned as that of the research problem. It crystallizes in a sharp increase in this phenomenon of "social media anxiety" by Jordanian youth, where it stressed JY's passion for using SM's platforms. Furthermore, depending on what had been observed, the massive impact of this problem on youths makes us much more interested in this study to specify the degree of influence of this issue on social media anxiety. This paper contains a massive objective, which is understanding how social media would be contributed to their health and youth lives. The paper discussed the usage of a considerable percentage of social media platforms by Jordanian youths. It has been the focal point for discussion among the specialist and educators. This paper discussed the issues concerning social media anxiety in academic and education literature, from SM's problems from the  Jordanian youths' lives. The paper concludes that no effort has been made for Jordanian youth to look into social media anxiety, which has many consequences for society.
ARTIFICIAL INTELLIGENCE AS A SERVICE IN DISTRIBUTED MULTI ACCESS EDGE COMPUTING ON 5G EXTRACTING DATA USING IOT AND INCLUDING AR/VR FOR REAL-TIME REPORTING
Authors: Madduru Pavan
Published: March 2021

To meet the growing demand for mobile data traffic and the stringent requirements for Internet of Things (IoT) applications in emerging cities such as smart cities, healthcare, augmented / virtual reality (AR / VR), fifth-generation assistive technologies generation (5G) Suggest and use on the web. As a major emerging 5G technology and a major driver of the Internet of Things, Multiple Access Edge Computing (MEC), which integrates telecommunications and IT services, provides cloud computing capabilities at the edge of an access network. wireless (RAN). By providing maximum compute and storage resources, MEC can reduce end-user latency. Therefore, in this article we will take a closer look at 5G MEC and the Internet of Things. Analyze the main functions of MEC in 5G and IoT environments. It offers several core technologies that enable the use of MEC in 5G and IoT, such as cloud computing, SDN / NFV, information-oriented networks, virtual machines (VMs) and containers, smart devices, shared networks and computing offload. This article also provides an overview of MEC's ​​role in 5G and IoT, a detailed introduction to MEC-enabled 5G and IoT applications, and future perspectives for MEC integration with 5G and IoT. Additionally, this article will take a closer look at the MEC research challenges and unresolved issues around 5G and the Internet of Things. Finally, we propose a use case that MEC uses to obtain advanced intelligence in IoT scenarios.
A NOVEL MULTI GRANULARITY LOCKING SCHEME BASED ON CONCURRENT MULTI -VERSION HIERARCHICAL STRUCTURE
Authors: Dr. Vivek Jaglan Ms. Swati, Dr. Shalini Bhaskar Bajaj,
Published: March 2021

We present an efficient locking scheme in a hierarchical data structure. The existing multi-granularity locking mechanism works on two extremes: fine-grained locking through which concurrency is being maximized, and coarse grained locking that is being applied to minimize the locking cost. Between the two extremes, there lies several pare to-optimal options that provide a trade-off between the concurrency that can be attained. In this work, we present a locking technique, Collaborative Granular Version Locking (CGVL) which selects an optimal locking combination to serve locking requests in a hierarchical structure. In CGVL a series of version is being maintained at each granular level which allows the simultaneous execution of read and write operation on the data item. Our study reveals that in order to achieve optimal performance the lock manager explore various locking options by converting certain non-supporting locking modes into supporting locking modes thereby improving the existing compatibility matrix of multiple granularity locking protocol. Our claim is being quantitatively validated by using a Java Sun JDK environment, which shows that our CGVL perform better compared to the state-of-the-art existing MGL methods. In particular, CGVL attains 20% reduction in execution time for the locking operation that are being carried out by considering, the following parameters: i) The number of threads ii) The number of locked object iii) The duration of critical section (CPU Cycles) which signiﬁcantly supports the achievement of enhanced concurrency  in terms of  the number of concurrent read accesses.
DESIGN OF HIGH THROUGHPUT ADD COMPARE AND SELECT UNIT FOR LOW POWER VITERBI DECODER
Authors: Ramesh Babu Chukka Sudhakar Jyothula, Vijaya Sree Ganta ,
Published: March 2021

The main purpose of this paper is to focus on the design of Viterbi Decoder (VD) with low power, which is significant for receiver section of data communication applications such as Radar, Satellite, Telephone and Automatic speech recognition. The Viterbi decoder algorithm consists of three most important blocks – Branch Metric Unit (BMU), Add Compare and Select (ACS) Unit and Survivor Memory Unit (SMU). BMU computes the metrics between the input and output state transitions. ACS unit include the Path Metric Unit (PMU), which computes the metrics with the sequence to a next state of a path and selects the lower metric value as a survivor path. SMU stores the data bits which utilizes the trace back method to fetch the likelihood path from the current state to a previous state. An ACS unit is an essential block for VD. The basic recursive ACSU design consists of Ripple Carry Adder (RCA), Comparator and a Selector block, which consume more area, power and operates with high junction temperature. To overcome these drawbacks, a modified ACSU design is implemented with recursive cancellation technique. ACS unit is modified by including a trace back mechanism to obtain a low latency and high speed in VD. It is designed with low complexity multiplexers, adders, logical AND gate and comparator block. This breaking recursive ACSU design utilizes less power, high throughput, low latency and also operates at low temperature. This analysis and simulation process are accomplished using Vivado Design Suite.
Effect of Transformational , Transactional Leadership and Job Satisfaction : Evidence from Information Technology Industries
Authors: Achmad Rozi, Bagaskoro, Joko Nugroho, Evalina, Jamalus Denok Sunarsi, Vip Paramarta, Munawaroh,
Published: March 2021

This study aims to measure the effect of leadership style (transformational or transactional) on job satisfaction mediated by job perceptions. Data collection was carried out through quantitative questionnaires with simple random sampling via electronic online to the employees of the Information Technology Industries in Banten. The results of the questionnaire returned and valid were 377 respondents. Data processing used the SEM method with SmartPLS 3.0 software. The results of this study concluded that transformational leadership has a positive and significant effect on satisfaction, both directly and indirectly through mediating job perceptions. Meanwhile, transactional leadership has no significant effect on employee satisfaction, either directly or indirectly through mediating job perceptions. This new research proposes a model to build job satisfaction for consumer goods industry employees in Banten through increasing the transformational leadership ability of company heads with perceptions of work as a mediator.
Investigation of the Performance of Model Predictive Control for Induction Motor Drives
Authors: Yehia Sayed Mohammed, Ahmed A. Zaki Diab Ahmed G. Mahmoud A. Aziz, Hamdi Ali,
Published: March 2021

The current work presents speed, torque and flux control of an induction motor (IM) drive, founded on model predictive control (MPC). Via the MPC techniques, the motor electromagnetic torque and flux linkage are controlled as an internal loop. However, the speed is controlled as the external loop. The internal control loop is founded on finite control set FCS-MPC, and the external control founded on the torque PI controller. The performance of the MPC is tested with various conditions of the drive operation, and the outcomes approve the excellent steady-state and dynamic operation of the system in a wide range of speeds and with torque disturbance.
A COMPARATIVE STUDY OF VARIOUS WAVELET APPROACHES USED IN IMAGE DENOISING
Authors: Laavanya Mohan Vijayaragahvan Veeramani,
Published: March 2021

The world is constantly changing, and vision helps the humans to understand the environmental changes over time. The changes can be seen by, capturing the images. Hence digital image plays a vital role in day to day life. During the process of acquisition of digital image, the qualities of digital pictures are degraded due to additive noise known as adaptive white Gaussian noise. Therefore, the major challenge of image denoising algorithm is to improve the visual appearance while preserving the other details of the image. For the last two decades, wavelet has become an elegant tool in image denoising techniques. Among all wavelet based denoising methods, wavelet thresholding became popular because, wavelet appropriately separates the noisy signal from the image. The wavelet separation leaves the coarse grain noise in approximation sub-band and fine grain noise in detail sub-bands. Therefore, in wavelet based thresholding methods noise in detail sub-bands are threshold and approximate sub-band noise are kept as such. Hence, the efficiency of all wavelet based shrinkage techniques depends on, the choice of threshold parameter, thresholding technique and how the noise in the approximation sub-bands are handled. This paper presents a brief comparative study of denoising techniques proposed in the research articles based on the above parameters for Gaussian noise reduction using various wavelets transform. With the help of these experiments, we are able to identify the strengths and weaknesses of these methods, as well as seek the way ahead towards a definitive solution to the long-standing problem of image denoising.
REVIEW ON PREDICTING DISEASE SEVERITY – LEARNING ALGORITHMS AS CLASSIFIERS FOR DATA WAREHOUSE ENVIRONMENTS
Authors: Dr. Suman Mann Miss SakshiHooda,
Published: March 2021

When research fields such as biological and medical arenas are considered, computer science-based applications have been utilized on an increasing basis. In the process, data has accumulated in significant amounts. In particular, most of the big data that has resulted has accrued from experimental and medical results that are reported. The eventuality is that in the healthcare platforms, data mining techniques have been employed towards timely predictions of conditions with which patients present, a process achieved through data analysis. Also, the data analyses have strived to discern correlations among disease-related variables or parameters in otherwise voluminous information. It is also notable that data mining has received increasing adoption in the healthcare field because of the perceived ability to provide room for informed decision-making, reliable treatment decisions, and the detection of potential fraudulence when it comes to health care service payments. While these interplays point to a promising trend regarding the utilization of data mining and disease severity prediction, the eventual bid data has proved to be so complex that the use of traditional methods of data processing and analyzing is deemed less effective. In addition, given data warehouse, the use of traditional methods towards data extraction makes it difficult to discern some of the hidden intersections, pointing to the importance of a new approach for data classification and disease severity prediction. The aim of this review paper was to demonstrate how the use of classifiers in the form of machine learning algorithms could be utilized in the prediction of the severity of diseases. Indeed, the context of the research entailed warehouse environments. To have an ideal kernel established towards data classification (that would then enable additional diagnostic tests), some of the machine learning algorithms that were investigated included SVM (support vector machine) and ANN (artificial neural network). From the review outcomes, it was established that when machine learning algorithms are used to obtain trend similarities in disease severity through predictive analytics, similar severity magnitudes of the given diseases could be projected in terms of their probabilities of and trends in occurrence. Also, the review outcomes demonstrated that disease severity prediction through machine learning algorithms is an insightful practice because it allows for the prediction of patients’ conditions, as well as the prevision of room for optimal decision-making regarding treatment modalities to be adopted by healthcare firms.
A NOVEL PRIVACY PRESERVATION MECHANISM FOR DATA AND USER IN DISTRIBUTED SERVERS
Authors: Dr. N. Dhanalakshmi, Dr. A. Thomas Paul Roy Dr. D. Suresh,
Published: March 2021

Advances in sensing and monitoring science allow location-based purposes however they additionally create tremendous privateness risks. Anonymity can supply a excessive diploma of privacy, retailer provider customers from dealing with carrier providers’ privateness policies, and limit the carrier providers’ necessities for safeguarding non-public information. However, guaranteeing nameless utilization of location-based offerings requires that the particular region facts transmitted via a person can't be without difficulty used to re-identify the subject. This paper provides a middleware structure and algorithms that can be used by using a centralized place dealer service. The adaptive algorithms regulate the decision of region data alongside spatial or temporal dimensions to meet distinct anonymity constraints based totally on the entities who can also be the use of place offerings inside a given area. Using a mannequin based totally on car site visitors counts and cartographic material, we estimate the realistically anticipated spatial decision for extraordinary anonymity constraints. The median decision generated with the aid of our algorithms is a hundred twenty five meters. Thus, nameless location-based requests for city areas would have the identical accuracy presently wanted for E-911 services; this would supply enough decision for wayfinding, automatic bus routing offerings and comparable location-dependent services.
TRUST COMPUTATION USING BOTTOM-UP PARSER APPROACH IN MANET
Authors: Et al. Dr. S. Varalakshmi
Published: March 2021

In Mobile Ad hoc NETwork (MANET), the nodes are able to move from one point to other freely since it is a decentralized network. The objective of the proposed scheme is to remove the malicious nodes and to select the highly trusted nodes for transmitting the data. The node entering in to the network is checked for its identity either it is malicious or normal by applying bottom-up parser approach. The node is checked for their trust levels by using shift reduce operations which protects the sensed information from the malevolent node in the network. Therefore, the trusted source node sends the confidential data to the sink through the trusted nodes in the network. The simulation analysis is carried to prove the efficiency of the proposed scheme.
A STUDY ON CUSTOMER PERCEPTION TOWARDS LIFE INSURANCE CORPORATION OF INDIA (WITH SPECIAL REFERENCE TO KUMBAKONAM TOWN)
Authors: V. RAJINIKANTH Dr. T. UNNAMALAI,
Published: March 2021

This study shows at how consumers feel towards life insurance companies in Kumbakonam. Since people are becoming more aware of their own mortality and the insurance industry is becoming more competitive, it is important for insurance providers to consider their customers' needs. The primary goal of this research is to determine customer satisfaction with LIC. This research is based on primary data collected via a questionnaire from 150 policyholders in Kumbakonam, and the data was analysed using percentages and the chi-square test to determine the factors that influence customer satisfaction with LIC.
CLOUD BASED MULTI-LANGUAGE INDEXING USING CROSS LINGUAL INFORMATION RETRIEVAL APPROACHES
Authors: Et al. Chayapathi A R
Published: March 2021

The exponential growth of data sizes created by digital media (video/audio/images), physicalsimulations, scientific instruments and web authoring joins the new growth of interest in cloud computing. The options for distribution and parallelization of information in clouds make the retrieval and storage processes very complicated, especially when faced with real-time data management. The quantity of Web Users getting access to data over Internet is expanding step by step. An enormous measure of data on Internet is accessible in various languages which could be accessed by anyone whenever. The Information Retrieval (IR) manages finding valuable data from a huge assortment of unorganized, organized and semi-organized information. In the present situation, the variety of data and language boundaries are the difficult challenges for communication and social trade over the world. To tackle such obstructions, CLIR, the cross-language information retrieval frameworks, are these days in solid interest. The Query Expansion (Q.E.) is the way toward adding related and important terms to original inquiry to upgrade its indexing ability to improve the significance of recovered files in CLIR. In this exploration work, Q.E. has been investigated for a Hindi-English and Kannada-English CLIR in that Hindi and Kannada queries are utilized to look through English docs. After the interpretation of query, recovered outcomes are positioned making use of OkapiBM25 to organize the most important doc at the top for expanding the significance of recovered docs using QE. We proposed architecture for Hindi-English and Kannada-English CLIR making use of QE. to improve the importance of recovered reports. In the primary investigation, QE. is performed with and without OkapiBM25 ranking. The outcomes show that the pertinence of recovered archives is higher with OKapiBM25 as contrast with the one without positioning. The work docs plainly demonstrate that the presentation of Hindi-English and Kannada-English CLIR framework can be improved altogether with query development using fitting terms located at suitable place and the recovered Snippets can incredibly fill in as the continuous test collection.
INSPECTING THE ROLE OF EMPOWERMENT, OSTRACISM, DEFENSIVE SILENCE AND EMPLOYEE PROACTIVITY WITH RELATION TO EMPLOYEE CREATIVITY AND ORGANIZATIONAL PERFORMANCE
Authors: Dalain Dr. Ali
Published: March 2021

Purpose: This study attempts to gain insight into what factor influence on employee creativity and perceived organizational performance. Therefore, a theoretical framework is developed grounded in empowerment theory workplace ostracism and defensive silence.  In addition to that the moderating role of employee proactivity is hypothesized between employee creativity and perceived organizational performance.

Design/Methodology/Approach: The research design of this study is based on positivist paradigm and followed quantitative research approach. A survey was administered towards public sector organizations in Saudi Arabia. Sample size of this study is computed with prior-power analysis using G-power software. For data analysis 384 responses were analyzed with structural equation modeling (SEM).

Findings: Results indicate that employee creativity is predicted by empowering leadership, psychological empowerment, structural empowerment, trust in leadership, defensive silence and workplace ostracism and explained  52.4% variance in employee creativity. Therefore, employee creativity and employee proactivity shows  55.1% variance in perceived organizational performance. Effect size analysis showed that structural empowerment had medium level of effect size when predicting employee creativity.

Practical Implications: This study contributes to empowerment theory and enriches the innovative and leadership literature. Practically, this research suggested that managers and policy makers should focus on empowering leadership, structural empowerment, psychological empowerment, employee proactivity and trust in leadership in order to boost employee creativity and perceived organizational performance.

Originality/value: This research is significant as it extends the empowerment theory with defensive silence and workplace ostracism and extends the body of knowledge on this subject. To the best of researcher knowledge this study is the first that test the empowerment theory with the moderating role of employee proactivity to determine employee creativity and perceived organizational performance.
FEATURE BASED ANALYSIS ON THE DUPLICATE FAKE ACCOUNT ON TWITTER USING INTELLIGENT MECHANISM
Authors: Alhariri Dr. Mohammed Ali
Published: March 2021

The duplicate fake accounts are detected in this work the data from the social media platform is accessed. The platform choose to use the analysis on social media platform is selected as twitter. The twitter data is accessed using Twitter API, with using some selected features that remain the most appropriate regarding the reason of duplicate fake account. The feature based analysis is compared using machine learning techniques, Random Forest, Decision Tree, and SVM. The performance is further analyzed based on accuracy SVM performed 93.3% accuracy, where decision tree performed as 89.0% and random forest performed as 85.5%. The better performance observed using feature-based analysis is of SVM.
A STUDY ON CUSTOMER PERCEPTION AND ATTITUDE TOWARDS 3D VIRTUAL REALITY SHOPPING
Authors: Byram Dr. Anand
Published: March 2021

An innovative technology application that creates textures of real store shopping experience may generate a rapid momentum in the e-retailing sector. In virtual reality (VR) stores consumers can not only virtually walk anywhere, but they can also interact with the virtual reality store by, right, left, up and down and looking around thereby viewing the shop. There is a comparison difference between existing in user boundaries and Computer-generated systems in 3D environment. creating a simulated artificial world, the computer virtually makes sensory feelings as much as possible, like hearing, vision, touch, even smell. The virtual reality web store could redefine the experience of online shopping and allows in creating an immersive experience that renovate the physical shopping voyage, that from home or anywhere that customers desire. Virtual Reality in Retailing is an innovative proposal that helps the customer to look around the store with a complete 360-degree view along with the controller that helps in the purchase. perceived entertaining and fun that felt by the shoppers to the extent to which online shopping they do. Better informational base helps the consumers to make more informed decisions that make them increase their satisfaction in the online shopping process perceived ease of use could implicit as the degree to which the online shopping customer have faith in that shopping will be free. the effect of the price perception and found impact of price perceptions on behavioral-intention. privacy is a conscious effort of e-retailor in dealing with customer data and information.
MEASURING THE EFFICIENCY IN LENDING OPERATIONS OF STATE CO-OPERATIVE BANKS IN INDIA
Authors: Dr. Sony Vijayan Devi Sekhar R,
Published: March 2021

Co-operatives are member driven and democratic in nature and played a pioneering role in providing agricultural credit to a very large rural population, especially in remote areas. The State Co-operative Banks (SCBs) are the apex level co-operative credit institutions and play a significant role in the functioning and growth of the co-operative credit structure in the respective states. Financial assistance from Reserve Bank of India and National Bank for Agriculture and Rural Development (NABARD) are mainly routed through the state co-operative banks and hence they have to ensure efficiency in their lending operations. The study has used the benchmarking technique DEA and revealed higher deviations in efficiency scores among state co-operative banks. The problems and challenges of SCBs differ from state to state. Hence in every state, the management and the regulating authorities should work on transforming the co-operative credit system into a viable and self- sustaining system by identifying and addressing their core inefficiencies.