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The banking business has forever been quite possibly of the most sought-after calling. Since the post-autonomy time frame,agreeable banks have inclined toward something very similar.Just a solid and effective Co-employable banking framework can develop and support in the opposition.Subsequently,the banking framework should not exclusively be not difficult to utilize, yet in addition equipped for meeting new difficulties raised by technology and other outside or inward factors.Human resources,whether private or public,are the main resource of any organization.They are blessed with such remarkable characteristics as creative mind, critical thinking skill, and the capacity to propel collectively.It's just a question of dealing with, assessing, and motivating them.This must be managed by consistently evaluating their outcomes. The deliberate, successive, and unprejudiced assessment of an employee's greatness in issues connecting with his current job and his possibilities for a superior job is known as performance appraisal.Toward the beginning of the performance appraisal process, the undertaking, needs, and expected performance principles for employees are laid out.These are then utilized as criticism and performance assessment strategies.Banking sector in India with its fast development in the quantity of branches and the new capabilities allocated to them have begun feeling tension on their organizational capacities. The different important factors contributing towards the adequacy of an organization and featuring the organizations capacity are the cycles of recruitment, placement, preparing, advancement and appraisal. Appraisal is one of the significant factors of organizational capacity, hence author has chosen this particular topic to reveal an outline on the importance of Employee Assessment in the Banking Sectors of India. It is based on both theoretical and analytical based study

Objective Breast cancer and breast tumors have been considered to be the most pervasive form of cancer in medical practice. Breast tumors are life-threatening to women, and their early detection could save lives with the proper treatment. Physical methods for detection of Breast Cancer are time-consuming and often prone to a misdiagnosis at classifying tumors. Recent trends in radiological imaging have significantly improved the efficiency and veracity of breast tumor classification. Artificial intelligence techniques could be used as an automated detection and classification system. Materials and methods In this research, we propose a novel configuration of a Stacking Ensemble with custom Convolutional Neural Network architectures to classify breast tumors from ultrasound images into ‘Normal’, ‘Benign’, and ‘Malignant’ categories. Results After thorough experimentation, our ensemble has performed with an accuracy, f1-score, precision, and recall of 92.15%, 92.21%, 92.26%, 92.17% respectively. Conclusion The presented ensemble leverages three Stacked Feature Extractors coupled with a characteristic meta-learner to provide an overall balanced classification performance, with better accuracy and lower false positives. The architecture works in association with gaussian dropout layers to improve the computation and an alternative pooling scheme to retain essential features.

The idea behind the work is to check for the residence of pesticide degrading genes in plasmids and to transform them on an expression vector allowing them to degrade the pesticide more efficiently. Seven different commercially used toxic pesticides were used for bioremediation study. Around 20 organisms were first isolated and based on their pesticide tolerance, 11 organisms were further used for analysis. Each of the organisms was first subjected to plasmid curing and confirmed for their ability of pesticide degradation ability on plasmids. From the isolated 6 organisms based on their efficiency, 3 isolates containing plasmids were further chosen. Their plasmids were extracted and transformed to E.coli DHα. Upon subjecting them to agarose gel electrophoresis the result implies the successful transformation of only two plasmid DNA to the host cell. The pesticide degradation rate of the genetically modified strain was found to be higher than that of the wild type adapted strain. Morphometeric and germinating ability of Venia radiata was tested in pesticide contaminated soil and was found to support the growth of the plant.

Orthogonal frequency division multiplexing (OFDM) or multicarrier modulation is an essential signal processing technique in new generation wireless gadgets owing to its potential to support fast and spectrally efficient transmission. One of the major limitations of OFDM systems is the peak-to-average power ratio (PAPR) of transmit data. In this article, a novel meta heuristic algorithm called modified grey wolf optimizer is used to boost the computing performance of subcarrier phase factor search in the undisputed partial transmit sequence method. The proposed modified grey wolf optimizer (mGWO) has a balancing between exploration and exploitation phases while searching for peak power carriers and brings out a nearly optimal performance but with less number of iterations. The objective is to propose low complex computing algorithm without compromising the output quality. The simulation results of proposed mGWO-PTS model assure improvements around 20 to 25 percent from that of the comparative counterparts such as GWO-PTS, PSO-PTS, and etc.

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

Image fusion is a one of the enhancement technique which is used to take the decision the images by the various types of sensors. Image fusion is nothing but the combination of two images which is helps to improve the quality of the image. In this paper, visible image and Infrared image are combined to acquire the informative image. Before and after image fusion, a new transformation technique is introduced to improve the quality of the image. To prove the quality of the image after applying new transformation technique, the fusion is done by four different techniques is used like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Non-Subsampled Contourlet Transform (NSCT) and Dual Tree Complex Wavelet Transform (DT-CWT). The comparison of following parameter values such as Entropy, Standard deviation, Mean gradient, Average pixel intensity and spatial frequency shows that proposed method is better to improve the image quality.

Wireless networks are inevitable constituents of the present communication world. The applications of wireless networks are numerous from fundamental communication establishment to high secured financial transactions. Providing security to communication and network transactions is an essential factor to protect the network environment from prying eyes of intruders. This work is targeted to provide a fast and reliable security protocol against intruder attacks based on the network behavior pattern analysis. Fuzzy logic is incorporated in this work to enable the swift security policy selection based on the behavioral pattern changes. Proposed functional blocks such as Node Behavior Analyzer (NBA), Cluster Behavior Analyzer (CBA), Integrated Network Behavior Tracker (INBT), Inversed Elegant Pairing Key Exchange (IEPKE) and Fuzzy based Security Policy Selector (FSPS) are commingled in the name “Behavior based Fuzzy Security Protocol for Wireless Networks” (BFSPWN). Improvements in standard wireless networks’ performance metrics such as Throughput, Latency, Jitter, End-to-End Delay and Security are considered in the evaluation process of proposed method.

Focal cemento-osseous dysplasia (FCOD) is a benign fibro-osseous lesion of bone characterized by the replacement of normal bone by fibrous tissue and subsequently followed by its calcification with osseous and cementum-like material. It is mostly asymptomatic in nature and requires no treatment. When secondarily infected, it becomes symptomatic and intervention is required. Here, we report a case of symptomatic FCOD of mandible in a 52-year-old female patient. Histopathological evaluation of the biopsy specimen by ground sections and decalcified sections aided in the final diagnosis of the case.

Background and objective In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. Methods The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. Results The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. Conclusion This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

The development of information technology shifts the users to access different services and perform various activities through Web services. The Web services provide easy adaptation and accessibility to the users. As the Web services are loosely coupled, they have more chance of being caught by middlemen and subject to various threats. To improve the security of Web services, there are many approaches being discussed, but lags with security performance. To improve the security session-based random selection padding scheme is discussed in this paper. The method initializes the elliptic curve and splits them into a number of sections. At each session, a specific section is selected randomly and a single key will be used as the private key for the session. The selection of the key will be performed randomly. Similarly, based on the numeric value of the region selected, a padding scheme will be used. The proposed method reduces the time complexity of key generation and verification. Also the proposed method improves the performance of the Web services.

Studying network communication within the neurons is the next step toward exploring the complexities of the human brain. In this research, we used electroencephalography (EEG) to study the neurophysiological processes because of their high temporal and spatial resolution. EEG stands out to be a vital modality in assessing patients with brain abnormalities like Autism, Epilepsy, Dementia, and Parkinson's. Nowadays, a large number of children worldwide is affected by Autism Spectrum Disorder, which impairs the ability to communicate and behave. In this research, we generate the connectivity models using the EEG signal dataset of Autism and normal children. Here, connectivity models are presented into a graphical form using different measures like phase synchronization, classical measures, granger causality, and information theory. These parameters were used to analyze the variation between Autistic and typically developed children.

One of the developmental disorder found in early childhood is Autism Spectrum Disorder (ASD). Kids suffering from ASD are affected by the way they act in society and interact with others. Usually, ASD kids are associated with excess or poor emotional facial-expressions. The primary focus of this paper is to use advanced deep learning techniques to classify ASD kids into two classes namely Low ASD kids and High ASD kids. Low and High here mentions the intensity of the ASD disorder in the kids. The proposed work aims at achieving this classification with the computer vision techniques and by learning on their facial expressions. Several videos of Low ASD kids and High ASD kids were collected. Each frame of these videos was then parsed into images to train and test an Attention based Residual Neural Network. This proposed model brings a novel method of embedding Attention mechanism on Residual Convolution Neural Networks, which results in carrying the most significant features from the initial and primary layers to the very end with very little distortion. This is done by the attention block, which weighs every parameter according to its significance. This way, the features with higher weighs are passed till the deep layers of the network without any loss of information. Thus, the proposed work is able to classify effectively Low and High ASD kids based on the videos collected successfully yielding results with a state-of-the-art accuracy of around 94%.

Children with neurodevelopmental disorders are increasing gradually every year. One in 100 children are diagnosed with brain function disorder. There are wide categories of disorder such as attention deficit hyperactive disorder, learning, autism spectrum disorder (ASD), etc. In this work, the focus is on ASD, its clinical methods, and analysis in various research works. ASD is a neurodevelopmental disorder which affects the intellectual functioning, social interaction (adaptive behavior), and has a specific obsessive interest. At present, there is no known cure for ASD, but the level of the pathological condition can be reduced when it is detected early. Early detection is tough and challenging till date. Many researches were carried out to ease the early detection for clinicians. Each method has its own merits and demerits. This chapter reviews and condenses various research works and their efficacy in analysis for the early diagnosis and improvement in children with autism.

Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.

Flying colors is a Hotel Booking Application; this application provides different staying solutions to customers, it is to book customers a hotel room online based on date and location along with the type of hotel. This architecture is based on agile development which is specially designed to work on multiple regions, channels, and quick future enhancements. PEGA7 Business Process Management (BPM) Tool which enables significant enhancement in building the application and data management experiences mobile functionality, user experience, and analytics. The goal of developing this application using PEGA is to improve the efficiency and performance, the liveliness of usual processes in the business. An automated system is developed to deliver high security and end to end delivery of works and speeds up the process drastically.

There are vast Image Fusion (IF) procedures are being projected in many ways by different analysts. Each coordinate point, it means pixel-level spatial space IF strategies, for the most part, lead to differentiate decrease. Pyramidal IF plans with the end goal like Laplacian, inclination, discriminate, quantity of-low-pass, and the morphological pyramids, and so forth abandonment to existing any spatial direction selectivity in the disintegration technique, also later regularly source obstructive impacts. The generally applied Discrete Wavelet Transform (DWT) can keep ghostly statistics efficiently, however, it will not be able to communicate spatial attributes adequately. Thus, DWT based grouping tactics will not be able to save the striking highlights of input images effectively and present antiques and irregularities in the intertwined outcomes. As of late, a limited Multiscale Geometric Analysis (MGA) instruments were grown, for illustration, Curvelet, Contourlet, NSCT, and so on which don’t experience the ill effects of the issues of wavelet. There are many IF and MIF techniques that work dependent on MGA devices were likewise projected. It is hard for the doctor to analyze the malady from the MRI and CT image independently for the patient. So in our proposed work, the MRI and CT clinical image are converted into a 3-D image for a better finding of the illness influenced part progressively precise and clear.

The information gathered from singular hubs is collected at a base station or host PC. To lessen vitality utilization, numerous frameworks additionally act in-organize the conglomeration of sensor information in the middle of the road hubs on the way to the base station. Most existing total calculations and frameworks do exclude any security arrangements, and subsequently, these frameworks are defenseless against a wide assortment of assaults. To address the security issue, propose one improved new technique. Secure data aggregation provides security to network from an attacker. Secure information accumulation procedure can likewise improve the power and exactness of data. Iterative separating calculation is one of the calculations to perform secure information accumulation and gives security to the whole system. Iterative sifting calculation all the while total information from numerous sources and give trust appraisal of these sources, through relating weight factor appointed to the information given by each source. Iterative sifting calculation with vitality model is utilized for giving more exactness to the Wireless sensor arrange condition.

Learning the electrophysiological activities inside the human mind is a significant step toward studying the human brain. Systems, such as electroencephalography, are significant instruments for considering the neurophysiologic activities, in view of their high value of temporal and spatial resolution. In the biomedical research, identifying brain abnormalities such as autism spectrum disorder through electroencephalography (EEG) signals is an extremely exhausting issue for specialists and human services experts. The high volume of data available with EEG will be a useful biomarker for the classification of autism and typical children. Traditional techniques face challenges to deal with such big data. So we present a strategy for autism identification by analyzing the EEG signal through mathematical model. One such modeling using graph theory is applied in this work. The EEG signals are acquired from 3 autism and 3 typical children. The functional connectivity among the neuron regions are plotted through small world networks. From this graphical models using a software tool Gephi, the graphical parameters as betweenness centrality, degree, weighted degree, closeness centrality, modularity, and clustering coefficient are calculated. There is significant difference among these parameters between autistic and typical children.