Reviewerly/exHarmony huggingface dataset:
Development of Agriculture Chatbot using Machine Learning Techniques

Agriculture data is a main source of country’s economic growth. It is important to provide agriculture related information to all the people who are involved in agriculture activities as and when required. This meaningful information is used by people who supply services to agriculture domain and to take some correct decision related to agriculture to apply for their field. The solutions to this problem are given by the efficient interaction of computer with human. Chatbot system provides ability to extract the exact answer to the queries posed by farmers. The proposed system is called as Agriculture Chatbot system or even it is called as Question-Answering system for agriculture domain, where farmer is asking the agriculture related question which fetches the precise answers for the asked questions by farmers in natural language and processes the query using RNN (Recurrent Neural Network) deep learning algorithm to extract correct answer.
AGS: a precise and efficient AI-based hybrid software effort estimation model

To predict the amount of effort to develop software is a tedious process for software companies. Hence, predicting the software development effort remains a complex issue drawing in extensive research consideration. The success of software development process considerably depends on proper estimation of effort required to develop that software. Effective software effort estimation techniques enable project managers to schedule software life cycle activities properly. The main objective of this paper is to propose a novel approach in which an artificial intelligence (AI)-based technique, called AGS algorithm, is used to determine the software effort estimation. AGS is hybrid method combining three techniques, namely: adaptive neuro fuzzy inference system (ANFIS), genetic algorithm and satin bower bird optimisation (SBO) algorithm. The performance of the proposed method is assessed using a well standard dataset with real-time benchmark with many attributes. The major metrics used in the performance evaluation are correlation coefficient (CC), kilo lines of code (KLoC) and complexity of the software. The experimental result shows that the prediction accuracy of the proposed model is better than the existing algorithmic models.
Diagnosis of Cervical cancer using CLAHE and SGLDM on RGB Pap smear Images through ANN

Cervical cancer is one of the most preventable Cancers in the present day. PAP and HPV tests are followed for early diagnosis for screening procedure. In medical field, it is difficult to detect Pap smear images through microscope. Image processing is a vital role to take right decision by using various technique and algorithms. In this paper, the proposed technique is used to enhance the Pap smear images by comparing Histogram Equalization in Contrast Stretching algorithm, Power Law Transformation for Gamma Correction, Shading Correction, Contrast Limited Adaptive Histogram Equalization (CLAHE). The Quality measurement MSE, PSNR, SC and NAC value has been calculated to find performance analysis of enhanced Pap smear images. Then proposed four different feature extraction algorithms SGLDM- Spatial Gray Level Difference Method, RDM- Run Difference Method, LBP- Local Binary Pattern and HOG-Histogram of Oriented Gradients are used to extract features of Pap images. Experimental results are obtained for a data set of 215 Pap images taken from the Management and Decision Engineering Laboratory (MDE-LAB) database. MATLAB R2016a used as a programming tool. ANN Classification is used for each feature extraction algorithm to evaluate the accuracy level. Thus, CLAHE achieved the good result for enhancement and SGLDM feature extraction algorithm reached 93% accuracy using ANN.
The Mediating Effect of Employee Performance on Transformational Leadership Style and Organisational Innovation: A Case Study of the Hotel Industry in Saudi Arabia

Rapid changes in the global environment, as well as the fierce competition among hospitality companies to obtain competitive benefits are well-prepared leadership, high-performing employees and innovative approaches. Leadership, employee performance, and innovation are key factors that help the hotel industry remain competitive and provide excellent services. This paper aims to investigate the mediating effect of employee performance on transformational leadership style and organizational innovation in the hotel industry in Makkah and Madinah, Saudi Arabia. In this regard, studies that are related to the hotel industry in Saudi Arabia are scarce. This research, therefore, contributes to the hospitality industry studies, as well as the hoteliers. Seventy-two local and international five-star hotels in Makkah and Madinah are selected for the study analysis, and rank-and-file employees are targeted as the respondents. A total sample of 371 respondents is studied. A Google link containing the survey form is administered to the employees through the departments’ heads at the selected hotels. The obtained data are statistically analyzed using SmartPLS. The findings showed that there is a positive mediation of creativity between intellectual stimulation and inspirational motivation on product innovation. This will, in turn, promote organizational innovation.
VLSI Implementation of Lossless Video Compression Technique Using New Cross Diamond Search Algorithm 

Compression is the reduction in size of data with the purpose of save space or transmission time. There are two types of compression Lossy Data Compression and Lossless Data Compression. Lossy data compression, that allow only approximation of the original data to be reconstructed from the compression. Lossless data compression that allow the original data to be reconstructed decompression that improve the performance of a computing system from the compression. Video Compression use in modern coding techniques to reduce redundancy in video data. In this paper the proposed New Cross Diamond Search algorithm is used for video compression(lossless). Two types of techniques are used in this algorithm, they are (1) Block Motion Matching Technique and (2)Motion Estimation and Compensation. By using this new cross diamond search algorithm, the number of checking point is less when compare to another algorithm, using 9 checking point for calculating the sum of absolute difference to determine the least value.By using this least value only the motion vector will be found. compare to diamond search algorithm, checking point is less. The New Cross Diamond Search Algorithm is 40% speed without affecting picture quality.
Exploring the effects of non-local blocks on video captioning networks

In addition to visual features, video also contains temporal information that contributes to semantic meaning regarding the relationships between objects and scenes. There have been many attempts to describe spatial and temporal relationships in video, but simple encoder-decoder models are not sufficient for capturing detailed relationships in video clips. A video clip often consists of several shots that seem to be unrelated, and simple recurrent models suffer from these changes in shots. In other fields, including visual question answering and action recognition, researchers began to have interests in describing visual relations between the objects. In this paper, we introduce a video captioning method to capture temporal relationships with a non-local block and boundary-aware system. We evaluate our approach on a Microsoft video description Corpus (MSVD, YouTube2Text) dataset and a Microsoft research-video to text (MSR-VTT) dataset. The experimental results show that a non-local block applied along a temporal axis can improve video captioning performance on video captioning datasets.
FPGA based Data Hiding through Steganography

Nowadays, the information security has been the key factor in communications, computer systems, electronic commerce and data sharing. One of the well-known methods for procuring the security of shared information using carrier files is steganography. The carrier file can be discrete such as image, text, audio and video etc. Digital images are the most commonly used format among those due to the high capacity and availability frequency. The hidden data is stored in an indistinct carrier in image steganography, i.e the digital image is used as a cover image to mask the secret message known as stego image. Cryptography can be then adapted for increasing the security of the stego image. A zig-zag MSB-LSB slicing based steganographic algorithm is proposed in this paper for concealing a secret image in a cover image. Power report and device utilization summary of the algorithm is calculated and the output is demonstrated on the VGA screen using BASYS3 Field Programmable Gate Array (FPGA).
Searching for cell signatures in multidimensional feature spaces

Despite research on cervical cells since 1925, systems to automatically screen images from conventional Pap smear tests continue to be unavailable. One of the main challenges in deploying precise software tools is to validate cell signatures. In this paper, we introduce an analysis framework, CRIC-feat, that expedites the investigation of different image databases and respective descriptors, particularly applicable to Pap images. This paper provides a three-fold contribution: 1) we first review and discuss the main feature extraction protocols for cell description and implementations suitable for cervical cells; 2) we present a new application of Gray level run length (GLRLM) features to Pap images; 3) we evaluate 93 cell classification approaches, and provide a guideline for obtaining the most accurate description, based on two current public databases with digital images of real cells. Finally, we show that the nucleus information is preponderant in cell classification, particularly when considering the GLRLM feature set.
Survey Paper on Gender and Emotion Classification using Facial Expression Detection

In day-to-day life Computer Vision is a domain in itself which deals with lots of application ahead like health care, banking, industrial, agriculture and many more in retail and security field. To gather the information about mood of any individual the main focus is on face. It is generally seen that humans depict their emotions through facial expression. At times, instead of verbal communication emotions are expressed by face. Here, along with it emotion classification nowadays gender classification is merged. Basically ‘LK method’ and ‘Pyramidal LK’ method were used for face detection. Both gender and emotion classification are less explored through face detection using Convolutional Neural Network (CNN) but in this paper it is analyzed that both classifications can be done in a single blend using state-of-art technology i.e. Multilayer Convolutional Neural Network (M-CNN).
Innovative possibility of a textbook for reading

The author substantiates the conceptual foundations and method concepts of writing a new reading textbook as a component of the educational provision of the language and literary education area. The requirements of the new normative base, theoretical provisions of modern didactics and methods regarding the process and results of the educational recipients that reflect a new understanding of education quality in the current socio-economic and educational conditions of the Ukrainian school development are taken into account. The resultative approach has fundamentally changed the technology of constructing the content of school education, determined the basic role of competence and environmental approaches, the modern implementation of the principles of child-centricity, the subject-subject of learning, the saving attitude to human and natural resources. Accordingly, the methodological concepts and methods of implementation in the textbook of three content lines: “Interact Verbally”, “Read”, “Explore the Media” are highlighted. Requirements of the content line “Interact Verbally” realized due to the variability of situations of educational interaction: collective, group, paired, use of innovative tasks (preparation by pupils of oral journals, participation in stage productions, performances, development of a creative product, etc.). Tasks for the formation of auditory skills and pupils’ understanding of the text are suggested. The means of implementing the content line “Read” are disclosed. Frequency is the task of developing a complete reading skill covering the development of its correctness, consciousness and expressiveness. The author used the task of developing pupils’ critical thinking and emotional intelligence: self-asking questions to the text, expressing evaluative judgments, searching in the texts of arguments that confirm or deny their own judgments about the actions of the characters, connection with pupils’ life experience, etc. The role of techniques for predicting the content of a work by the title, drawings, converting verbal information into diagrams, models as a means of text analysis is revealed. The content line “Explore the media” is implemented in the textbook by two means: through the innovative form of presentation of information “MediaWindow” and a number of media tasks to the texts. This approach helps pupils to learn the concepts of media, information, and experience in the creation of elementary media products: flyers, advertising, posters and more
PI and Fuzzy Logic Controller based Comparative Analysis of Separately Excited DC Motor

Separately excited dc motors (SEDCM) are generally used in laboratory and industrial applications due to their high efficiency, performance response and high torque. An idea is proposed in this paper by applying the PI and Fuzzy Logic Controller (FLC) to control the speed of SEDCM. Simulink model with and without PI and FLC has been developed in Matlab to analyze the performance of dc motor. Based on simulation results it is said that control characteristics of FLC are better than the conventional PI controller. The modeling of SEDCM and execution of the controllers is carried out in MATLAB.
Case Study on Effectiveness of Moocs Lessons for the Rural Technical Education

India is a vast country, constitutes 66.46 % of rural population. Technical education is one of the common choice for young students of the nation, especially for the rural India. Massive Open Online Courses (MOOCs) is one of the greatest initiative from the government of the India through MHRD. The case study work is to verify the effectiveness of this great initiative especially for the rural Indian students. The case study work constitutes a survey by the name of “Effectiveness of MOOCs” from the different level students who appeared the MOOCs lectures during their technical education. The work is to identify the key difficulties to choose MOOCs lessons during the graduation and its effectiveness to improve their knowledge level especially for young rural Indians. The survey also contributes the student suggestion to improve the effectiveness of this Nobel (i.e. MOOCs) program.
Semantic Analysis

Natural language understanding has gained significant importance in the last decade with the advent of machine learning and further advances like deep learning and artificial intelligence. Computers, or machines in general, can be programmed to learn specific things or perform specific operations. However, the key limitation is their inability to perceive, understand, and comprehend things like humans do. With the resurgence in popularity of neural networks and advances made in computer architecture, we now have deep learning and artificial intelligence evolving at a rapid pace and we have been engineering machines into learning, perceiving, understanding, and performing actions on their own. You may have seen or heard several of these efforts in the form of self-driving cars, computers beating experienced players in their own games like Chess and Go, and more recently chatbots. So far, we have looked at various computational, language processing, and machine learning techniques to classify, cluster, and summarize text. We also developed certain methods and programs to analyze and understand text syntax and structure. This chapter deals with methods that try to answer the question, "Can we analyze and understand the meaning and sentiment behind a body of text?"
Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis

The early prediction of sepsis is important for ICU patients, as the risk of mortality increases as the disease is left untreated.We hypothesize that there is a need to learn important feature representations, such as to extract salient information from sepsis data.In this paper, we propose an unsupervised method to learn spatial-temporal information from the data, through the use of two autoencoders.For the official 2019 PhysioNet Challenge, our team, Kent Ridge AI (ranked 77th), obtained a utility score of -0.164 on the full test set.Additionally, we report crossvalidation results and identify several issues which can potentially help to improve performance.
Fairness-based heuristic workflow scheduling and placement in cloud computing

Cloud computing has become a commercial business environment for small and large scale industries to avail the resources. Cloud application requests are processed dynamically to access resource utilisation based on the diverse demand for cloud resources. The expected allocation of resources to the application is inequitable. This paper aims to analyse the QoS metrics of cloud services based on user satisfaction, and proposes Fairness-based Heuristic Workflow Scheduling and Placement (FHWSP) algorithm with the new multi-objective function. It minimises the overall profit and time of execution with respect to characteristics of tasks and physical servers in the datacentre. The evaluation of the proposed FHWSP algorithm with the existing traditional algorithms is simulated in a CloudSim simulated environment. The experimental results significantly improve the QoS parameters in terms of 5% in makespan and 3% in total execution cost of the scheduling and placement algorithm on CloudSim environment.
Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection

© 2019 IEEE. The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been utilised to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, color variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve better GP individuals having multiple trees influencing each other's performance during the evolution, in order to get overall performance gains. The performance of the new method is checked on two benchmark skin image datasets, and compared with six widely used classification algorithms and the single tree GP method. The experimental results have shown that the proposed method has significantly outperformed all these classification methods.
Measuring Size of an Object using Computer Vision

In our research paper, the original size of an object present in an image is calculated. The choosed algorithms are used computer vision are carried out, it includes basic tools that are efficient enough to find the size of an object. In this project, we proposed the measuring of dimensions of an objects present in the picture and the distances between the objects is computed. Computing the measurements of the objects in a picture is similar to finding the distance to an object from the camera. First of all, we have to find the value of pixels per a given metric. To find this value, we have to select one reference object from the image by considering two major properties. First property is that dimensions (width &amp; height) of the reference object should be known and in terms of (inches or mm) and the other property is that identification of the reference object should be easy
Analysis of Success Factors in The Implementation of ERP System In State Owned Enterprise Case Study PT. XYZ

PT XYZ is a state-owned company that implements SAP-based ERP system as the part of its IT Governance. However there are many problems that occur since the implementation process is carried out, there are a lot of user complaints related to the system and some users do not understand the use of the system which results in obstruction of their work (blockers). This cause some negative impacts on the company, especially related to financial aspects and employee productivity. Therefore, it is necessary to conduct a study to evaluate what factors influence the success of the implementation process and after that this research will suggest recommendations related to these factors. This study uses a research model adapted from D&M Model, TAM, and ERP CSF. Research variables in this study are Information Quality (IQ), System Quality (SYQ), and Service Quality (SEQ), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Project Success (PS). Data processing and analysis methods in this study used a statistical descriptive analysis approach and SEM-PLS variant analysis. The results of this research are, PS positively affected by PU and PE, PE is positively affected SYQ and SEQ, and PU is positively affected by SYQ, SEQ, and IQ.
An Efficient Method for Associative Classification using Jaccard Measure

Classification is a data mining technique that categorizes the items in a database to target classes. The aim of classification is to accurately find the target class for each instance of the data. Associative classification is a classification method that uses Class Association Rules for classification. Associative classification is found to be often more accurate than some traditional classification methods. The major disadvantage of associative classification is the generation of redundant and weak class association rules. Weak class association rules results in increase in size and decrease in accuracy of the classifier. This paper proposes an efficient approach to build a compact and accurate classifier by using interestingness measures for pruning rules. Interestingness measures play a vital role in reducing the size and increasing the accuracy of classifier by pruning redundant or weak rules. Rules which are strong are retained and these rules are further used to build the classifier. The source of the data used in this paper is University of California Irvine Machine Learning Repository. The approach proposed in this paper is effective and the results show that the approach can produce a highly compact and accurate classifier