Reviewerly/exHarmony huggingface dataset:
Competitor Identification by Use The Sentiment Classification Based on the User Research

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.
An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

In accordance to the Fourth Industrial Revolution, the emergence of the digital transformation alongside with the collaboration between networked machines and human beings in decision-making has attracted much attention globally. The utilization of conversational agent to convey information on behalf of human have gained popularity and been used extensively. As a result, this research intends to examine and to innovate the current corporate website by providing the capabilities to deliver instantaneously replies and reliable information through the integration of a conversational agent via deep learning, comparable to communicating with the competent customer service consultant. This study is reliant on the Artificial Intelligence (AI) to offer natural language processing (NLP) by presenting retrieval-based model and Deep Learning to enable the conversation agent to make smarter and better decision in generating reliable and up-to-date responses. Furthermore, this research intends to automate the process of minimizing the need for human interference, specifically the botmaster to perform the knowledge maintenance manually.
Face Recognition using Active Appearance and Type-2 Fuzzy Classifier

Face recognition in unrestrained surroundings has turn out to be more and more prevalent in numerous applications, namely, intelligent visual surveillance, immigration automated clearance system and identity verification systems.The conventional pipeline of a contemporary face recognition system usually consists of face alignment, face detection, feature classification, and representation.In this paper, the input images for face recognition are subjected to feature extraction using Active Appearance Model (AAM).In addition, Type 2-Fuzzy classifier is adopted for classifying the images.Moreover, the proposed scheme is compared with Neural Network, k-NN (Nearest Neighbor) and Type 1-Fuzzy classifiers and the results are obtained.
Electrocoagulation Treatment for Removal of Color and Chemical Oxygen Demand in Landfill Leachate using Aluminum Electrode

The present research work mainly deals with the removal percentage of Color and Chemical Oxygen Demand (COD) on landfill leachate by using electrocoagulation (EC) process. An EC process was carried out with an aluminium electrode and it act as both anode and cathode. The study mainly targets the factors affecting on electrode material, electrolysis time, initial pH, applied voltage, inter-electrode distance. The experimental result reveals that there was raise in BOD/COD ratio from 0.11 to 0.66 and the maximum percentage removal achieved were COD and Color 78.4% and 77.0% respectively. The optimum inter-electrode distance 1cm with electrode surface area 35 cm2 and optimum electrolysis time of 90 min at optimum applied voltage 10V, stirring speed 250 rpm and pH is 9.3. These results showed that the EC process is appropriate and well-organized approach for the landfill leachate treatment.
Digitalisation Trends and Blended Learning Visualisation in Modern Digital Education

The article under review actualizes the problem of using digital computer technologies in the process of blended learning. The notion of «blended learning» is determined and specified according various sources. Various models and ways of organizing this kind of work as an alternative to the standard form of learning are presented. The article presents the positive experience of using blended learning technology. Attention is paid to visualization as one of teaching tool to create educational materials for blended learning. Synthesis of verbal (especially word) and visual elements (pictures, graphic notes, animations, films, plots and diagrams, etc.) within the limits of one text’s space is an instrument of optimization of the process of semantic perception and understanding text information. All benefits of this modern technology allows to establish a holistic adoption of this model by students and the positive direction for the development of blended learning as an innovative teaching technology
Lossless Tamil Compression using ASCII Substitution and Modified Huffman Encoding Technique

Tamil language is a longest existing classical language in the humankind. It is one of the scheduled languages in India and also official language for many countries. Communication using Tamil language is drastically growing after the practice of internet. Storage of Tamil documents also emerged greater than before. So there is a high requirement for data compression to improve the efficiency of storage and fast communication of Tamil documents. This research paper provides a novel approach for Lossless compression technique especially for Tamil documents. The compression process involves three major steps: separation of English alphabets appears with in Tamil text, substitution of ASCII in the place of Unicode Tamil characters using static dictionary and building a Huffman tree with a variation method for encoding the Tamil document. Performance of Tamil compression is measured by finding the space efficiency of memory storage needed to store the compressed file. The space efficiency can be measured by finding the parameters of compression ratio, compression factor and percentage of compression. Time efficiency is calculated by finding the time taken by the algorithm to compress and decompress a file. The average compression achieved through this compression technique is 72.08%. The decompression process restores the original file without any loss of data.
Lessons learned from being BOLD: Staff experiences of an institutional strategic project in Blended and Online Learning Development

In recent years, tertiary institutions have engaged more with digital education, especially blended and online learning. One institution sought to transform its existing provision, and enhance learning and teaching, through a substantial strategic funding initiative comprising almost 20 individual projects.&#x0D; To capture the learning from this substantial investment, 20 semi-structured interviews were carried out with staff involved in setting up or working on these projects, typically early adopters. Interviews sought to revisit motivations to engage with the project, and capture perceived benefits including lessons learned, challenges, enablers, support needs and recommendations going forwards.&#x0D; Participants were motivated to enhance their own teaching practice and improve student learning experience/outcomes. Recognised benefits included enhanced learner experience, transformed teaching practice, flexibility and access, teaching efficiencies, and developmental opportunities for staff. Significant challenges related to the time required to design, develop and implement courses, as well as technological and student-related issues. Despite these challenges, participation in the project led to the development of new skills and transformed teachers’ learning and teaching practice. This was made possible through a number of enablers including prior experience of blended learning, and supportive others from across various services, schools and colleges.&#x0D; The study highlights the importance of strategic projects in expanding the digital capabilities of an institution to meet the expected—and unexpected—demands of the future. The findings are discussed in the context of the current literature on blended and remote learning, and existing institutional frameworks that support transitions to blended learning.&#x0D;
Sistem Pakar dalam Mengidentifikasi Penyakit Kandungan Menggunakan Metode Forward Chaining Berbasis Android

Maternal Mortality Rate (MMR) in Indonesia is very high, so that maternal health problems are a national problem. This problem needs to get top priority. The health of pregnant women is crucial for the growth of the fetus they contain. Pregnancy can cause a decrease in maternal resistance. This decrease will trigger the arrival of various diseases. For that we need a system that can identify uterine diseases quickly and accurately. This study aims to identify uterine diseases in pregnant women based on symptoms experienced. This identification is the initial information that is useful to support the decision to take preventative action. Data processed in this study were 20 patients. This data is sourced from the Sungai Melati City Clinic which goes to an obstetrician, Dr. Yandi Zulkarnaen, SpOG. The method used in processing data is Android-based Forward Chaining. The results of this study include the name of the disease, description of the disease, and treatment solutions. After testing and calculating the level of system accuracy, a good degree of accuracy is obtained from the system calculation results with an expert decision of 90% of the 20 test data. Based on the level of accuracy, the expert system is very precise in identifying uterine diseases quickly.
Comparative Study of Data Mining Classifiers with Different Features and Different Databases Domain

Data mining is a multi-billion dollar global market that is gaining popularity.Data mining is an interdisciplinary field, which originated from statistics, data visualisation, data bases, and machine learning.There are many learning algorithms used in data miningassociation rules, decision trees, neural networks, genetic algorithms, support vector machines etc. Anyone with a basic understanding of data visualisation techniques, statistics and computer science can easily get started with data mining.More important is an understanding of scales of measurement, data preparation and transformation techniques, data storage technologies (data bases and data warehouse), and Online Analytical Processing (OLAP). DATA MININGData mining is the process of extracting hitherto unknown and potentially useful patterns, trends, anomalies and rules from stored historical data for business promotion, decision making or classification.Data mining is an inter-disciplinary field with roots in enterprise decision support.Exploratory Data Analysis (EDA) is a similar technique for summarising and identifying patterns in data.But EDA is often applied on small volume of data generated by sampling, direct observations or controlled measurements and analysed using purely statistical techniques.The results obtained by a data mining process are used in marking business decisions and short-term predictions.It has diversified into many other fields that have no business context.For example, SVM is used to give a categorical label to unseen data instances using a model obtained from a set of labelled training data.It has more applications in business than in medicine, biology, genetics, etc.,
The design and implementation of a wearable human activity recognition system based on IMU

In recent years, with the rapid development of the inertial measurement unit (IMU) and wireless body area network, and the maturity of pattern recognition theory, the technology of human activity recognition gradually gets the attention of researchers, becoming the research hotspot in this field. On the basis of existing IMU, this paper designs a wearable human activity recognition system, which consists of microprocessor, three-axis accelerometer, three-axis gyroscope, power module and so on. The system can provide real-time, continuous human motion information (acceleration and angular velocity information) to the Android control unit, and it can realise the real-time receiving, dynamic display and storage of human motion information.
Classifying COVID-19-related hate Twitter users using deep neural networks with sentiment-based features and geopolitical factors

Anti-Asian hate tweets caused by COVID-19 pandemic is an ongoing social problem in the USA and around the world. Although existing studies have been done by using a text classifier, little is known on how deep learning works with public sentiments of political opinions and geographical diversities. This paper provides a new method to classify the pandemic-related anti-Asian hater on Twitter. A novel dataset for tracking pandemic-related Twitter users, which contains more than 10 million tweets, is created in this study. Target users are annotated by identifying their sentiments towards the US elections with their geolocations. The empirical result indicates that the political sentiments and the county-level election results make significant contributions to the model building. By training a DNN model, over 190,000 Twitter users are classified as hate or non-hate with a 61% accuracy and a 0.63 AUC score.
Identification of fingers on the basis of Hamiltonian cycles of local features

The problem of finding the lengths of Hamiltonian cycles on complex graphs is considered. The task has such practical applications as determining the optimal routes (salesman's task), identifying graph structures (recognizing the characteristics of local features of biometric objects), etc. When solving the task of verification of biometric samples, the problems of addition or disappearance of reference points, deformation of the distances between them, the appearance of linear and angular displacements of the whole sample emerges. Using the method described in the article, the problem of displacements can be eliminated, as the solution is stable when shuffling of the points is present. Moreover, it is possible to obtain reference plans with the same stability. Obtaining them requires less computational complexity and provides greater recognition accuracy. A detailed description of the problem solution based on the application of the method of branches and boundaries for symmetric matrices of graphs, which describe the distribution of local features in the images of fingerprints, has been proposed. It is known that a guaranteed solution for finding the length of the Hamiltonian cycle for an arbitrary graph of the planar distribution of points is possible only by using an exhaustive search. However, the computational complexity of such a search is not acceptable. The method of branches and boundaries, like all existing methods of directional search, does not guarantee finding a solution with an arbitrarily large dimension of the graph. Therefore, a method of decomposing graphs is proposed, which allows reducing a complex problem to a set of simpler ones. That allows for a significant reduction in computational complexity. The relative invariance of the metrics of Hamiltonian cycles to probabilistic shifts, which are characteristic of biometric pattern recognition problems, has been shown.
Estimation of Solar Irradiance on a Contoured Platform for Optimized Solar Power Generation

Now-a-days, applications of flexible solar photovoltaic technologies are emerging in the fields of portable applications (solar tent, solar canopy, solar aircraft etc.). Easy integration with any contoured surface and lightweight property makes flexible solar PV technology (Amorphous Silicon, thin-film Silicon, Dye sensitized solar cell) to be a potent solution for multipurpose conditions. However, contour surface produces variable tilt on the SPV modules which causes non-uniform illumination on the integrated SPV array. Further, this leads to significant loss in power generation which requires optimization. This article carries out a numerical study to investigate the effect of surface contour on the incident solar irradiance and a devise a methodology for solar PV array layout strategy to reduce the power loss due to non-uniform illumination.
ScrAnViz: a tool for analytics and visualisation of unstructured data

Existing big data visualisation tools are meant for visualising structured data. But survey shows that about 80-90% of potentially usable business information is in unstructured format. Analysing unstructured data is challenging due to lack of structure and relational form. In this paper, we have proposed a tool called ScrAnViz that can structure data, perform analysis and provide visualisation thereby helps in decision making for business people and end users. An attribute based opinion mining algorithm has been developed and implemented. Performance analysis shows that the algorithm has reduced the search time by three times than the traditional document level sentiment analysis systems.
An Efficient Grouped Task Scheduling and Resource Allocation in Cloud Computing Environments

Task scheduling in cloud is the allocation of resources to a task at a particular time. In cloud, scheduling strategy is defined or adapted by a scheduler according to the changing environment. Allocation of resource with poor capacity to a task may lead to increase in execution time of the task. Problem of resource under utilization may also occur when a resource with high capacity is allocated to a task that requires a resource with lesser capacity. In this paper we proposed an Efficient Grouped Task Scheduling (EGTS) and resource allocation to minimize average waiting time, average execution time and increase resource utilization. EGTS classify Tasks into two groups of similar task type, and sort the tasks in the order of their respective deadlines. Task in each group is allocated Virtual Machine with capacity equal to the average capacity required by tasks in that group. An experiment was conducted using CloudSim to exhibit EGTS and the result shows minimal average execution time, average waiting time and a higher resource utilization compared to Min-Min and Max-Min.
Fraud investigations

While fraud is nonviolent, it is certainly not a victimless crime. This highlights the unique skill set needed for internal and external fraudulent investigations, the value of whistleblowers, the important of protecting whistleblowers, major fraud schemes such as the Nigerian fraud ring and organized crime ring. This chapter gives necessary skills for specific investigations.
Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

Image Forgery is an illegal activity in the society as per cyber laws. There are various types of forgeries in which forgery on images is considered as an illegal activity. Image forgery may take place in different ways. One way for doing forgery on images is copy and move forgery which may result in loss of image integrity or authenticity. There are number of popular detection techniques exist such as SIFT, SURF etc., but have high complexity in detection of forgery. Here we have proposed a method to detect the forgery on images which results in loss of integrity or authenticity. In our proposed method we have used descriptor matching using Trie Data Structure The descriptor matching method of implementation using Trie data structure made the complexity of the problem to reduce to O (n log n). Using Key points approach we can verify the integrity of the image. Extracting the features with key points approach is computational expensive task. But there is KAZE method which overcomes this situation. KAZE’s method of using non-linear diffusion filtering requires it to solve a series of PDEs. This cannot be done analytically forcing KAZE to use a numerical method called an AOS scheme to solve the PDEs. However, this process is computationally costly and therefore an accelerated version of KAZE was created. The Accelerated KAZE or AKAZE which creates non-linear scale space through Fast Explicit Diffusion for reduce the complexity in extracting the features.
Modeling the Intuitive Decision-Maker’s Mindset

Today, the term, Working Memory, is closely associated with intelligence.We propose that in addition to improving and speeding-up analysis, Artificial Intelligence (AI) can also be useful as a supplement to Working Memory.It is generally accepted that working memory plays a crucial role in cognition and models by computers, can help us understand the human mind.Building an artificial working memory can bring further benefits; for example, it can separate retrieval from reasoning and therefore, can acquire new concepts.The aim of this research is to solve the capacity shortage problem of Working Memory, by using AI as a supplement.In order to develop our argument, we characterize the ID3 algorithm as a way of looking for a consistent solution in the existing Case Based Graph; as the ID3 algorithm builds it from an empty graph, to an increasingly complex one.Methodologically, our study is based on observation of several Digital Natives (DNs) playing different games at Mobilis Interactive Exhibition Center in Győr, Hungary.The aim is to explore the behavior of the DN generation.By identifying the different mindset patterns of DNs, we will be able to observe how different DNs can be facilitated, to enjoy the games, rather than being bored, anxious or even, becoming addicted.
Logical Review on Educational Data Mining

Student's admission, attendance in the class and their examination results is one of the big data for today's educational institution for manipulating and storing.Maintaining this type of data is of the logic task for data recorder.As the education data is growing very quickly in the education field so that different logical algorithms are required.Due to this reason Educational Data Mining (EDM) is required in Educational Sector.The algorithms of data mining like traditional cannot applied directly to educational issues because they have particular aim, so that a primary algorithm has to apply then after the method of data mining will use on the problem.Before processing the data clustering can be used as a primary algorithm.The number of studies shows application of data mining algorithm by using different attributes.So, in this paper a logically review on applying clustering algorithm as well as its uses in terms of EDM is studied.