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Dementia is a brain condition that impairs the cognitive abilities of an individual. Mild cognitive impairment is a mediator phase of healthy and dementia controls. The motivation of this study is to predict dementia using magnetic resonance imaging data, which is significant for the diagnosis of normal control and dementia patients. The proposed model leverages effective methods like Discrete Wavelet Transform, Bag of Features, and Support Vector Machine. The four wavelets haar, Daubechies, symlets, and coiflets are used for image compression. The results of the proposed data intelligence model are promising in terms of accuracy which is 92.32% which is better than the recently proposed models. Also, the proposed data intelligence model is compared with the models which may use curvelet transform, and shearlet transform and with the methods which have gone without using DWT transforms. The comparisons have also been made with the models that have used other prevalent techniques like Principal Component Analysis, Fisher Discriminant Ratio, and Gray Level Co-occurrence Matrix. The outcomes support the usage of each technique in the suggested data intelligence paradigm.

Dementia is a neurocognitive brain disease that emerged as a worldwide health challenge. Machine learning and deep learning have been effectively applied for the detection of dementia using magnetic resonance imaging. In this work, the performance of both machine learning and deep learning frameworks along with artificial neural networks are assessed for detecting dementia and normal subjects using MRI images. The first-order and second-order hand-crafted features are used as input for machine learning and artificial neural networks. And automatic feature extraction is used in the last framework with the pre-trained networks. The outcomes show that the framework using the deep neural networks performs better contrasted with the first two methodologies used in terms of various performance measures.

The advent of Internet of Things (IoT) has created immense possibilities which are fundamentally altering the way healthcare services are provided around the world. In addition to improving accessibility of healthcare services and patient safety, this has also reduced healthcare expenditures and increased operational efficiency in the healthcare sector. In this regard, Cloud resources are widely used to support near real-time IoT based healthcare applications by performing efficient algorithms on the huge amount of data produced by medical sensor devices. However, response time along with data privacy and security still represent major concerns that inhibit Internet of Things (IoT) medical devices and frameworks from being entrusted as an efficient solution to attain the goal. Of late, there is a burgeoning interestedness towards development of fog and edge based frameworks as a way to counteract the shortcomings of the cloud. The paper offers a review of healthcare related developments from a variety of perspectives, including outbreak control, chronic disease management, neonatal and pediatric treatment, elderly care, and disability management. The paper further examines the challenges hindering the adoption of IoT based healthcare devices and outlines the future research directions.

Dementia is a brain disorder that causes loss of memory leading to disruption in the normal course of life of an individual. It is emerging as a global health problem in adults with age 65 years or above. Early diagnosis of dementia has gone forth as a key research zone with the aim of early identification for hindering the advancement. Deep learning provides path-breaking applications in medical imaging. This study provides a detailed summary of different implementation approaches of deep learning for detecting the disease. Transfer learning for multi-class classification has also been explored for detecting dementia. The pre-trained convolutional network, AlexNet is used with 3 optimizers, SGDM, ADAM, RMSProp. A Dataset of 60 MRI images is taken from the OASIS dataset. Accuracy of the methods has been compared and the best parameters including classifier, learning rate, and a batch size of the model have been identified. SGDM classifier with a learning rate 10-4 and a mini-batch size of 10 have shown the best performance in a reasonable time.

The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite challenging. The methodologies proposed in recent researches have performed well in segmenting glands from benign subjects but have not given satisfactory results when segmenting glands from malignant cases. The methodology proposed in this paper is based on the symmetric encoder-decoder network which works remarkably well in detecting and segmenting glands in the case of malignant subjects and overall. The proposed pipelines harness the power of multilevel CNN architecture to capture contextual information and concatenation of features from skip connections with upsampled feature maps to improve the localization accuracy thereby improving the precise segmentation accuracy. The raw predicted map is further refined using morphological operators as a post-processing tool. The method is trained and evaluated on the Warwick-QU dataset. The final segmentation results have been compared with the performance of top-performing teams of gland challenge (GlaS) MICCI 2015 along with the recent researches. The final predicted segmentation maps have achieved the F1 score of 0.81 for gland detection, object dice score of 0.82 for segmentation, and Hausdorff distance of 84.18 for gland shape similarity which is akin to or higher than the existing models for the malignant subject. The model has done affably well on the overall dataset too.

Background: Glaucoma and diabetic retinopathy are known to be the prime reasons for causing irrevocable blindness in the world. However, the complete vision loss can be obstructed through regular screening of the eye to detect the disorder at an early stage. Objective: In this paper, we have presented a novel nonlinear optimization technique that helps in automatic detection of the optic disc boundary from retinal fundus images thereby satisfying the anatomical constraints by using elephant herding optimization algorithm. Methods: In our approach, median filter has been used for the noise removal in retinal images. The pre-processed image is passed to the metaheuristic algorithm known as Elephant Herding Optimization algorithm. Results: The proposed technique for optic disc segmentation has been applied and tested on four standard publicly available datasets namely DRIVE, DIARETDB1, STARE and DRIONS-DB. The ground truth of the optic disc boundary has been collected from two experts of glaucoma: Expert A and Expert B who are specialists of glaucoma. The quantitative and qualitative analysis has been done to evaluate the performance of optic disc segmentation techniques. Conclusion: The proposed technique for optic disc detection helps to obtain the smooth boundaries in retinal fundus images. The aim has been successfully achieved by proposing an approach using EHO to achieve optimized solution. The effectiveness of the approach has been evaluated on for benchmark datasets and the acquired results have shown the accuracy values to be 100% for DRIVE, 100% for DIARETDB1, 99.25% for STARE and 99.99% for DRIONS-DB.

Introduction: The objective of the study was to find the levels of work life balance of f & b service captains working in five star hotels of Delhi, India. The captain is a supervisor in food service department and is responsible to ensure quality of service and guest satisfaction with the help of a team of waiting staff and under the guidance of manager. As the work characteristics in food service outlets are challenging and always create issues like stress, overwork and work life imbalance. This paper focus on the aspect of work life balance issue of captains working in different food service outlets within five star hotels of Delhi. Materials and Methods: To measure the levels of work life balance author had devised a questionnaire measuring four dimensions identified in explorative study, namely quality time, involvement, satisfaction and health. Cronbach alpha values were found to be 0.082 and the data collected was analyzed through SPSS software; statistical tests ANOVA, Tukey HSD, t test were applied to draw conclusions. Result: As per analysis of data it was found that work life balance of captains in f & b service department of five star hotels of Delhi is not satisfactory, and the results were similar for captains working in different types of food service outlets. Conclusion: The study conclude that captains working in different food service outlets in five star hotels of Delhi, India do not have satisfactory work life balance, also it was noted that work life balance were same irrespective of the gender, it was found that captains in the age group of 25-30 years have a marginally higher levels of work life balance in the total but not above satisfactory levels. The main reasons were identified as break shifts, stretched working hours and lower compensation. Keywords: Work life balance, Five star hotels, F & b service, Captains, Delhi.

Introduction: The objective of the study was to measure and compare the levels of work life balance of operational staff of the four main departments of five star hotels in Delhi. To analyze the levels of work life balance and compare whether there were a significant differences in the levels of work life balance of operating staff from different operational department of five star hotels of Delhi. Materials and Methods: The study is based on the data collected from 121 employees of five star hotels of Delhi, who work in four operational departments of hotels. The data was collected with the help of self-administered questionnaire and analyzed through SPSS software. Result: Analysis of data proved that there was a significant difference in the levels of work life balance experienced by operational staff of five star hotels, highest level of work life balance was experienced by staff of front office and lowest for the of food production department. The result followed a similar pattern for the four dimension of work life balance namely quality time, involvement, satisfaction and health. Conclusion: There was no equality in the levels of work life balance of operating staff of five star hotels, the levels of work life balance was differential among departments, it was highest for the staff working in front office department and then a gradual decrease was noted with each subsequent department namely housekeeping, food service and food production. The differences could be due to factors like shift timings, break shifts etc. Keywords: Work life balance, Five star hotels, Delhi.

This study intends to identify and evaluate the preference of tea varieties among the foreign tourists in India. The significant aspects which resulted in the selection of these tea varieties demar...

In this paper, a model is presented for classification of Dementia brain disease using magnetic resonance imaging. Bag of features (BOF) is used for extracting the features of MRI scans, which are classified using multi-class Support Vector Machine (SVM) for distinguishing the scans into three categories as demented, mildly cognitive impaired and normal controls. The Magnetic Resonance Images (MRI) used for the classification is obtained from ADNI database. The speeded up robust features are extracted using BOF approach. The experimental results showed the achievement of competitive performance in terms of classification accuracy using the proposed methodology. The BOF approach clubbed with SVM leads to an accuracy of 93%.

The abnormal growth of leukocytes causes hematologic malignancies such as leukemia. The clinical assessment methods for the diagnosis of the disease are labor-intensive and time-consuming. Image-based automated diagnostic systems can be of great help in the decision-making process for leukemia detection. A feature-dependent, intrinsic, reliable classifier is a critical component in building such a diagnostic system. However, the identification of vital and relevant features is a challenging task in the classification workflow. The proposed work presents a novel two-step methodology for the robust classification of leukocytes for leukemia diagnosis by building a VGG16-adapted fine-tuned feature-extractor model, termed as "LeuFeatx," which plays a critical role in the accurate classification of leukocytes. LeuFeatx was found to be capable of extracting notable leukocyte features using microscopic single-cell leukocyte images. The filters and learned features are visualized and compared with base VGG16 model features. Independent classification experiments using three public benchmark leukocyte datasets were conducted to assess the effectiveness of extracted features with the proposed LeuFeatx model. Multiclass classifiers trained using LeuFeatx deep features achieved higher precision and sensitivity for seven leukocyte subtypes compared to the latest research on the AML Morphological dataset, and it achieved higher sensitivity for all cell types vis-à-vis recent work on peripheral blood cells dataset from the Hospital Clinic of Barcelona. In a binary classification experiment using the ALL_IDB2 dataset, classifiers trained using LeuFeatx deep features achieved an accuracy of 96.15%, which is better than the other state-of-the-art methods reported in the literature. Thus, the higher performance of the classifiers across observed comparison metrics establishes the relevance of the extracted features and the overall robustness of the proposed model.

Background: Computer-Assisted Diagnosis (CAD) has become a common practice of use in the healthcare industry due to its improved accuracy and reliability. The CAD systems are expected to improve the quality of medical care by assisting healthcare professionals with a wide range of clinical decisions. A CAD system is a combination of Computer-Assisted Detection (CADe) and Computer-Assisted Diagnosis (CADx) system. Objective: The objective of this research article is to generate an optimized rule-set for medical diagnosis capable of providing improved accuracy. It is evident from the literature that keeping a balance between these performance parameters is a real challenge. Methods: In order to achieve the desired objective, the following two contributions have been proposed to improve diagnosis accuracy: 1) an unsupervised feature selection approach based on ACO Meta-heuristic is used to design the CADe system, and 2) an ACO assisted decision tree classifier technique is employed to make CADx system. Results: Three popular UCI (Wisconsin Breast Cancer, Pima Indian Diabetes and Liver Disorder) medical domain datasets have been used to evaluate the performance of the proposed model. The exploratory result analysis shows the efficiency of the proposed work as compared to existing work.

Purpose The purpose of this paper is to evaluate and analyse the street food preferences of foreign tourists in Delhi. It will also try to find out the reasons for the selection of these foods by the tourists. Design/methodology/approach The data collection was done from 670 foreign tourists at the departure gates of Indira Gandhi International Airport, New Delhi. This study involved location intercept technique through face-to-face interviews and filling of structured questionnaire for taking the responses. The street food preference of the tourists is analysed using analytical hierarchy process model. Findings This study identified 17 street foods which were amongst the most preferred by the foreign tourist, with chicken tikka being the most favoured and paddu being least preferred. It was also found that tourists usually prefer street foods which are mild in taste and are hygienically prepared. Practical implications This study suggests that Indian street food presents a huge market for the foreign tourists that needs to be nurtured. It will help the stakeholders in the street food businesses in Delhi to devise strategies to promote food tourism, modify and align tourism products, enhance farming techniques and ultimately improve the destination image and branding of the place. Originality/value This is the first attempt to try to explore tourists’ street food preference and will help in maximising the influx of foreign tourists, as the concept of culinary tourism is on rise in India.

Objectives: The goal of the present study is to describe how the transition to remote emergency delivery was addressed in three universities during the COVID-19 pandemic, to determine the satisfaction levels of their students and faculty with this new teaching-learning experience, and to gather their opinions about the future of higher education. Method: The study uses a mixed-methods approach, including faculty and student surveys and focus groups Results: The study shows high satisfaction with the emergency remote delivery and clearly reflects the relevance of enhancing the digital components of future learning experiences in higher education and a unanimous preference for hybrid education. Participants provide recommendations to institutions regarding what students and faculty would like to keep for a more effective learning experience when the new normal comes. Conclusions: COVID-19 has had terrible consequences; however, the pandemic has brought along some positive effects and improvement opportunities in higher education, and, if the results of the present study are any indication, the future of face-to-face higher education should be hybrid. Implication for Theory and/or Practice: The study results can provide recommendations and inform decision-making by institutional leaders and policy makers regarding the necessary enhancement of the digital component of the teaching and learning process in higher education.

Cloud Computing technology has become one of the most powerful technologies in terms of high availability, flexibility, and scalability. Besides all the widely perceived benefits of cloud computing, there is a concern that makes the use of the cloud leads to a potential risk that is security. To overcome security issues, a strong authentication mechanism is required. Biometric authentication is one of the very popular methods used for identity detection so that only an authorized person can access the system, one method to achieve this is image processing. In this paper, we review various optical image encryption techniques based on the optical Double Random Phase Encoding (DRPE) system, the oldest and most extensively investigated optical scheme. In the literature, we get an overview of digital image processing applications and data encryption schemes. The various parameter used to measure the image encryption level is discussed. The optical Double Random Phase Encoding method and its numerical simulation are then investigated. In addition to it, other optical encryptions techniques such as multiple image cryptosystem extension and different techniques are discussed that enhance the optical encryption security, which is used widely and generally appreciated. We explored the cloud framework via the biometric authentication process to authorize the legitimate user and permission checks is done by the biometric system. The level of security of the overall system increases when the authorization process is strong and faultless. The challenges faced in this area and the objectives of the research have been discussed.

Recent years have witnessed a great technological evolution in video display and capturing technologies leading to the development of new standards of video coding including MPEG-X, H.26X and HEVC. The cost of computations, storage and high bandwidth requirements makes a video data expensive in terms of transmission and storage. This makes video compression absolutely necessary prior to its transmission in order to accommodate for different transmission media's capabilities. Digital video compression technologies therefore have become an important part of the way we create, present, communicate and use visual information. The main aim behind a video compression system is to eliminate the redundancies from a raw video signal. The tradeoff involved in the process of video compression is between the speed, quality and resource utilization. The current chapter explores the techniques, challenges, issues and problems in video compression in detail along with the major advancements in the field.

Dementia is a brain disorder which causes loss of memory leading to disruption in the normal course of life of an individual. It is emerging as a global health problem in adults of age 65 years or above. The early diagnosis of dementia has gone forth as a key research zone having an aim of early identification for hindering the advancement. Artificial Intelligence, Machine Learning and Deep Learning have acquired a great importance in the medical field. Deep Learning provides path breaking application in medical imaging. This study gives a detailed summary of different implementation of deep learning for detecting the disease Dementia.

In this work, we have presented a new bio-inspired blood vessel segmentation hybrid technique using bird swarm algorithm (BSA) and river formation dynamics (RFD) algorithm. BSA mimics the behavior of the birds and RFD imitates the process of formation of riverbeds. The birds pass through each and every pixel while flying and foraging for the food in order to identify the edge pixels falling in their path. The path taken by the birds has been optimized with the help of RFD algorithm. The pixels are categorized into the edge or non-edge pixels by thresholding. The proposed technique has been implemented and evaluated quantitatively and qualitatively on STARE dataset for the analysis. The experimental results clearly showcase the remarkable improvement over a few metaheuristics and non-metaheuristic techniques namely flower pollination search algorithm, ant colony optimization algorithm and matched filter in terms of accuracy, specificity, and sensitivity. The defined approach gives promising results by detecting continuous and smooth blood vessels with an accuracy of 95.05%.

Purpose This study aims to find out various dimensions of the risk and benefit perceptions of the consumers of street food vendors. It will identify the reasons which affect consumer’s attitude and consumption patterns towards street foods, which bring about changes in their behavioural intentions (repurchase intention and word of mouth intention). Design/methodology/approach Five risk and two benefit factors were tested on a factor model by exploratory factor analysis using 26 constructs. Two-step approach was followed in which measurement model, having six constructs with 17 measurement items, were assessed, followed by the structural model. This study explained that the consumer attitude is affected by perceived risks and benefits. Further, the risk perception negatively affects the behavioural intentions. A conceptual model was framed to depict the relationships among variables and was empirically tested. Findings The results indicate that risk and benefit perception of consumers are not only inter-related but also responsible for their changes in attitudes towards the street foods. In the factorial analysis, it was found that perceived benefit factors, i.e. convenience and value, are responsible for positively influencing the attitude of consumers towards street food. The findings indicate that reducing risk perception and increasing benefit perception will positively change the patron’s attitude. Originality/value The data collection was done through a structured questionnaire specifically drafted to collect the relevant data for the study from the 658 street food consumers in Delhi. To examine the factorability of 26 items of risk/benefit perception, 586 observations were used.

Internet of Things (IoT) being a powerful integration of radio-frequency identification (RFID), sensor and wireless devices, has given a challenging yet powerful opportunity to shape the existing systems thereby making them intelligent. Abounding applications are developed in the recent years. Millions of physical objects are expected to be connected to form a system creating wide distribution network inferencing meaningful deductions from raw data. This survey paper is an effort to describe IoT along with its vision, possible application domains and key challenges faced in making IoT a reality. This paper presents current state-of-art of IoT in a systematic manner.

Nowadays, there has been recent interest in applying machine learning techniques to the neurodegenerative disorders. Dementia is one such emerging global health issue and its early detection can be very helpful. A comparative analysis of four machine learning algorithms i.e., J48, Naive Bayes, Random Forest and Multilayer Perceptron is presented in this paper. For the attribute reduction, the CFSSubsetEval is used. It has been shown that J48 is performing best among all the algorithms for the detection of Dementia.

The hotel industry is one of the most booming industry contributing tremendous growth in the global economy. It has never got affected by any kind of recession or economic turmoil, and this happens because of the fact that individuals/ families would need services of hotel industry for various reasons of human activities like business, recreation, pilgrimage educational tour, historical tours, festivals, carnivals, medical assistance trip etc. and so on. The biggest apprehension about this industry is attrition/turnover rate of employees; and to trounce this matter, selection of the right candidate at the right profile for the right post is the way to success. Selection criteria include all the essential and desirable skills, attributes, experience, and education which an organization decides is necessary for a position. Selection criteria help to select the most capable, effective, suited, experienced, qualified, the person for the job. Applicants must demonstrate and prove the ways in which they will be of valued for the job and the organization. Job selection criteria are also known as key selection criteria or KSC. They are designed to help make the most accurate match between the requirements of a position and the skills of an applicant. For selecting the right candidate, perfect for a particular job, selection has to be well planned, tactically accurate and strategically correct, as there is a huge pressure of short listing, filtering and selecting the right candidate, which makes the whole exercise lengthy as well as painstaking.

Dementia is a brain disorder emerging as a global health problem in adults of age 65 years or above. A lot of studies are going on for the early detection of dementia using various machine learning algorithms. In this study, we have implemented four machine learning algorithms as classifiers namely, Naive Bayes, Random Forest, Multilayer Perceptron and SMO in combination with various feature selection techniques to obtain the classification accuracy as a measure for the detection of dementia. It is found that Random Forest is showing best result of 98.6% when CFSSubsetEval Feature selection technique is used.

In this paper, a new two-step approach for enhancing the accuracy of initial search center (ISC) prediction in h.264 has been proposed which improves the speed of motion estimation in video encoding. Previous methods for estimating the ISC worked in a single step by finding the mean/median of motion vectors (MVs) of neighboring blocks of the current and reference frames. The major drawback of all the existing ISC techniques is that they consider the participation of all the neighboring blocks with equal probability without taking into account their correlation with the current block. The blocks which have least correlation or no correlation at all affect the accuracy of prediction and hence increase the chances of trapping the search in local minima. Moreover, in the existing ISC prediction techniques, participation of MVs of restricted neighboring blocks is considered, which further limits the prediction accuracy. To elevate these drawbacks, a new two-step approach has been presented. The first s...

For improving the accuracy and speed of motion estimation, a refined threshold estimation technique for zero motion prejudgment (ZMP) has been proposed in this manuscript. Various fixed or dynamically varying threshold techniques reported in literature treat all the blocks whose sum of absolute differences (SAD) lie below a particular threshold as stationary blocks (SB). However, in reality these blocks have high probability to be SB but not certain. Also SB with abrupt or irregular variations may not be detected with these techniques. The proposed two-level ZMP technique with its dynamic threshold at level 1 attempt to identify SB blocks with much more accuracy and precision. Level 2 of it has very high probability to identify the SB with large and abrupt distortions that are left in level 1. The proposed technique for ZMP can be applied before any fast motion estimation algorithm (FMEA). Adaptive rood pattern search algorithm (ARPS) is used as FMEA for comparing the performance of the proposed ZMP technique with other fixed and dynamic threshold techniques described in the literature. Simulation results clearly indicate that the proposed technique shows very promising results—significantly reduces the decision errors, improves the video quality in terms of PSNR and SSIM and reduces memory requirement for residual frames in comparison to fixed and dynamic threshold techniques for ZMP. Real-time applications contain sequences with higher frame rates as 30 and 60 fps. The proposed technique gives promising results on sequences with higher as well as lower frame rates. Further, proposed ZMP is able to save computations by 95–99 % in comparison to full search (FS) algorithm with small degradation in PSNR and SSIM. The proposed technique when embedded with fast motion estimation algorithm (ARPS) can reduce the computations up to 70 % depending upon the presence of stationary blocks with negligible degradation in video quality. All this has been achieved by enhancing the accuracy in determination of stationary blocks in comparison to other ZMP techniques.

Glaucoma is one of the diseases that damages the optic nerve of the eye and can result in permanent vision loss. Hence, it becomes essential to detect the disorder at an early stage. Optic cup segmentation from retinal fundus images is an important step for automated glaucoma diagnosis. In this paper, we have presented Glowworm Swarm Optimization algorithm that helps in automated detection of optic cup from retinal fundus images. The glowworms as agents help in the construction of the solutions by making use of the intensity gradient inside the cup region. The exploration capability of glowworms is derived from the adaptive neighbourhood behaviour, thereby making them capable of detecting optic cup region accurately, even in images having weak cup boundaries or low contrast. The proposed algorithm has been implemented and evaluated on RIM-ONE, DRIVE, STARE, DRIONS-DB and DIARETDB1 datasets for qualitative and quantitative analysis. The average overlapping error obtained is 22.1% for DRIONS-DB Database which is minimum as compared to other approaches namely thresholding based, Ellipse fitting and Ant Colony optimization algorithm.

Eye diseases like glaucoma and diabetic retinopathy are known to be the thieves of eye-sight that are responsible for causing the vision loss worldwide. Automatic detection of such diseases with the help of the digital color fundus photography helps in early diagnosis and treatment. From the fundus images, optic disc is required to be analyzed to diagnose the disease. In this paper, a technique has been proposed for locating optic disc through metaheuristic techniques namely Ant Colony Optimization algorithm, Bacterial Foraging Optimization, Firefly algorithm, Cuckoo Search algorithm and Krill Herd algorithm. A comparison has been made amongst all of them and also with existing disc detection techniques. The bacterial foraging algorithm has shown the best results as it has obtained 99.55% accuracy with DiaRetDB1 database, 100% accuracy with HEI-MED database, 100% with DRIVE database and 98% with STARE database.

A new approach based on fuzzy logic and ant colony optimization is presented for the reconstruction of curves from a set of unorganized points. Fuzzy clustering is used to reduce the number of points to cluster centres. Ant colony optimization is used to construct a travelling salesman path which is a closed curve. Extra edges are deleted and new edges are added using the fuzzy membership function. The algorithm presented in this paper has been used for reconstructing open as well as closed curves. The results obtained for multiple and self-intersecting curves are also good. Various examples for open, closed, multiple and intersecting curves with complicated shapes are shown to illustrate the significance of the presented algorithm.

The objective of the research was to find out work life balance issues for chefs at supervisory level in Hotels of Delhi. The supervisory level chefs are very critical to fulfill the requirement of the smooth operations for the different section of the kitchen or food production department. Supervisors have to monitor, guide and motivate the operative staff of any department or process; this becomes more important if it is food production department of a hotel. The objective of the study was to find out whether there exists a healthy work life balance in lives of supervisory chefs engaged in hotels of Delhi. The paper also try to evaluate the work life balance on the four dimensions of work life balance namely quality time, involvement, satisfaction and health. The study tries to analyze and identify the probable causes for work life imbalance and expected solutions for the improvement of work life balance. The author had devised a questionnaire to gather the data from the respondents, which included a set of statement on which participants agreement or disagreement was recorded. Based on the responses scores for each dimension were calculated and integrating the scores the overall levels for work life balance were ascertained. Study employed SPSS 18 for analysis of data, where data was analyzed using cross tables, descriptive statistics & t test to come to conclusions. The reliability for the questionnaire was ascertained through Cronbach alpha and coefficient of correlations. The results had clearly reflected that chefs at supervisory level have work life balance issues as their mean score is less than the average for all the dimensions also on overall score for work life balance the data clearly proves that the chefs at supervisory levels have low levels. The main causes which were found for work life imbalance were stretched working hours, break shifts, long travelling time and inability to get leaves. The main probable solutions identified to improve the levels of work life balance were increased salary, to compensate for actual hours of work, ease of getting leaves.

In most video sequences, especially containing slow motion, a large number of blocks are stationary. Early determination of these blocks may save large number of computations in any motion estimation (ME) algorithm. The decision for declaring a block to be stationary can be made by comparing the block distortion with a predetermined threshold whose large or small values may affect the speed and accuracy of a ME algorithm. Accurate prediction of this threshold proposes a challenging problem. In this manuscript, a dynamic two level threshold estimation technique has been proposed. This two level scheme not only detects constant variations in the neighboring blocks but is also capable of detecting stationary blocks with abrupt variations. Performance of the proposed technique is evaluated by implementing ZMP before ME process in adaptive rood pattern search (ARPS) algorithm. Simulation results show better performance of proposed technique in comparison to single level dynamic threshold predictor and fixed threshold predictor.

Soft Computing refers to the techniques of problem solving which are inspired from the human behavior, natural genetics and the behavior of insects. All these techniques are parallel computational techniques which aim to handle imprecise, incomplete, non-linear and complex data. This paper deals with one of the fields of soft computing- namely Ant Colony Optimization (ACO). ACO is a computational intelligence based approach which is used to solve combinatorial optimization problem. Due to its simplicity and optimal approach it has been applied to routing, scheduling, sub-set, assignment and classification problems.Focus of the current paper is onto the use of Ant Colony Optimization in the field of Image Processing. Edge detection, edge linking, feature extraction, segmentation and image compression are the various image processing tasks in which ACO has been applied successfully. The details pertaining to each of the approach have been discussed. Benefits of using ACO over the conventional techniques have also been presented.

Plagiarism has many meaning depending upon the seriousness of the task. It is piracy of content in the academic conduct and is marked as equivalent to a crime leading to disruption of reputation or much worse suspension. There are many examples from acclaimed universities to much publicized personalities those have been accused for plagiarism. This paper discusses various techniques and methods that have been adopted to detect and prevent plagiarism in articles, journals, scientific publications and the future perspective.