Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of New Pearson Correlation Coefficient for Recommendation

Recommender system helps the users to choose something actually want or need. So, this system uses the user-based collaborative filtering method for building movies recommendation system. This system knows the user’s interest and recommends items which are particular interest of the user. For this target, choosing appropriate similarity measure is a key to success the recommender system. New Pearson correlation coefficient (NewPCC) is one of the most popular similarity measures for collaborative filtering recommender system, to evaluate how much two users are correlated. By using NewPCC similarity method, this system finds out the recommended movie from all movies.
IMPROVE THE PERFORMANCE OF TRANSPORT LAYER OF TCP PROTOCOL IN WIRELESS SENSOR NETWORK

The Wireless Sensor Network is a network of many sensor nodes with wireless channels to communicate with others. With no centralized control and specified network connection, it can transfer to the outside world. At the same time, both nodes are capable of acting as a source or sink node. These nodes have minimal processing power due to their small physical size, limiting the processor capacity and the battery's size. When they work together collectively, they can gather information about the physical environment. They get a transceiver to communicate with the virtual world and with the real world. The routing topology to be used for the network depends on the transmission capacity of its nodes. It also depends on the location of the node, which may differ from time to time. This paper examines the characteristics of a wireless sensor network and analyzing the functionality of TCP protocol and its variables. It suggests using nodes near the sinks as proxy nodes to improve the WSN transport layer's efficiency. The results of the simulation show that the throughput increases.
Outcome of total thyroidectomy in non- malignant solitary thyroid nodule by FNAC

Introduction: Solitary Thyroid Nodules (STN) are a common entity and is detected in approximately 5% to 7% of the adult population by physical examination alone. 7 to 15% of thyroid nodules are harbor cancer. In this study, we will evaluate the outcome of total thyroidectomy in non-malignant STN by FNAC as regards to the histopathological results and post-operative complications. Patients ANALYSIS 24(105), September October, 2020 Medical Science ISSN 2321–7359 EISSN 2321–7367
Stock Market Integration using VECM: Evidence from Largest Economies

Globalization has resulted in increased co-movement of financial markets across the globe presenting profit making opportunities galore for international investors through global diversification. This study is an empirical application of daily data of Stock Exchange Indices of top ten largest economies of the world namely United States, China, Japan, Germany, United Kingdom, France, India, Italy, Brazil and Canada ( in nominal GDP terms) over a period of more than ten years ranging from Sep 15th, 2008 to Dec 31st 2018.This paper attempts to unearth the issue of stock market integration focusing on the top ten largest economies of the world based on the data released by International Monetary Fund (IMF) World Economic Outlook (April 2018). A considerable research has been done to establish the co-integration amongst the member countries of ASEAN, BRIC and SAARC but the originality value of the present study lies in checking cross country cointegration (if any) among the largest economies of the world. Various financial econometric models such as Augmented Dickey-Fuller Unit Root, Correlation, Johansen Cointegration, Wald Test, VECM and Variance Decomposition have been used to analyse the possible cointegration among the sample stock markets. The empirical results suggested that there exists weak long run dynamics among the selected stock markets.
Technical Report: Computation-Aware Bearings-Only Target Localization and Circumnavigation in 2D

A novel framework for computation-aware control is introduced that allows a mobile autonomous agent to act on intermediate iterates of its computations as it moves. We consider an iterative optimization algorithm running onboard the agent. Each iterate of the algorithm is the most recent estimate of an optimum, while a convergence rate bounds the distance from that iterate to an optimum. The agent implements a controller that acts on its most recent iterate in a way that is modulated by its convergence rate; an agent takes less decisive action for iterates far from an optimum and takes more decision action for iterates close to one. The problem of bearings-only target localization and circumnavigation in 2 dimensions is solved in a computation-aware manner, and we bound the error in control performance that is introduced by using online computations. Simulation results are presented to confirm the high performance of this framework for this problem.
ADHD EEG signal analysis using Machine Learning

2Assistant Professor, Department of Electronics and Telecommunications, Xavier Institute of Engineering, Mumbai, India 1,3,4,5Student, Department of Electronics and Telecommunications, Xavier Institute of Engineering, Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract – Traditionally after an EEG(electroencephalogram) test, the data is analysed only by an experienced neurologist(a doctor trained in nervous system disorders) and it takes at least 48 hours for the patient to get their results back. In some cases, it could take days for results to arrive. But with the help of Machine learning and Deep learning this task can be performed in considerably less time with decent accuracy. This will help neurologists to make more informed decisions about a person’s EEG data. Once it provides desired accuracy, it could significantly reduce the time taken to analyse EEG signals and thus will prove of great importance to the medical community. Our project involves building an ML model to analyse EEG signals of people suffering from Attention Deficit Hyperactivity Disorder(ADHD) and give results within minutes with maximum possible accuracy. The goal is to find out which data pre-processing techniques and Machine learning algorithms works the best to detect ADHD.
A Case Based Approach to Evaluation and Management of Simple and Mixed Disorders of Sleep

Citation: Abreu AR, Chediak AD. A Case Based Approach to Evaluation and Management of Simple and Mixed Disorders of Sleep. Ann Clin Case Rep. 2017; 2: 1394. ISSN: 2474-1655 Copyright © 2017 Chediak AD. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Case Series Published: 07 Jul, 2017
Natural Calamity Prediction

1,2,3UG Student, Dept. of Computer Engineering, Universal College of Engineering, Mumbai, India 4Professor, Dept. of Computer Engineering, Universal College of Engineering, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Thousands of human lives are lost every year around the globe, apart from significant damage to our property, animal life etc. due to natural disasters (e.g., earthquake, flood, hurricane and other storms, forest fire etc.). Natural disaster is thing which cannot be prevent, but they can be detected so that it can give people some amount of precious time to get to the safety. In this paper, we are using computer vision and Deep learning to detect natural disaster in image and video stream. We have proposed a system for detection of natural disaster with the help of Convolution Neural Network (CNN), which helps in extracting features of natural disaster more effectively.
CIVE648 Project: Bayesian Poisson Tensor Factorization for Learning Paratransit Mobility Patterns

Increasing transportation accessibility lacks equivalent research attention on paratransit for special population, like the disabled. Because understanding mobility patterns for customers is the first step towards a humanized transit service. We suggest unveiling travel behaviors of the disabled via Bayesian Poisson Tensor Factorization (BPTF), a powerful tool to analyze latent patterns of sparse, dispersed and discrete paratransit trip records. Temporal and spatial features including multi-peaks, weekday-weekend indifference and regular trip purpose are discovered. We wish our report can serve as trial to get into the travel behaviors of the special population and invoke more welfare and attention on their transit needs.
Soot Concentration and Temperature Measurements in Co-flow Laminar Propane-Air Diffusion Flames at Pressures up to 7 bar

The influence of elevated pressures up to 7 bar on the flame geometry and twodimensional soot temperature and concentration were studied in a propane-air laminar co-flow diffusion flame. Narrow band photography and two-color pyrometry in the visible spectrum have been used to gain a better understanding on effects of pressure on these parameters. The peak radiation from soot particles at 430 nm and 516 nm were collected by applying CH* and C2* narrow band filters in front of a digital CCD camera. It has been observed that the flame properties respond very sensitively to the pressure. As the pressure is increased, the flame diameter decreased at all flame heights. The average cross-sectional area of the flame shows an inverse dependence on pressure. Flame height behavior, however, was observed to be bimodal; initially as pressure rises increasing and then decreasing. The flame luminosity at the flame centerline increases, with distance from the fuel nozzle, then the flame became less intense with height due to cooling of the soot particles by radiative losses, leading to a smoking flame at pressures of 4 bar and above. The soot temperature results obtained by applying the two-color method are shown to be consistent with the pyrometry results. Soot temperature measurements show that in a propane diffusion flame the overall temperature, decreases with increasing pressure. It is shown that the rate of temperature drop is greater for a pressure increase at lower pressures in comparison with higher pressures. Soot formation was dramatically increased by pressure and the location of the peak soot volume fraction moved towards the flame centre line at higher pressures.
Hierarchical Text Classification of Urdu News using Deep Neural Network

Digital text is increasing day by day on the internet. It is very challenging to classify a large and heterogeneous collection of data, which require improved information processing methods to organize text. To classify large size of corpus, one common approach is to use hierarchical text classification, which aims to classify textual data in a hierarchical structure. Several approaches have been proposed to tackle classification of text but most of the research has been done on English language. This paper proposes a deep learning model for hierarchical text classification of news in Urdu language - consisting of 51,325 sentences from 8 online news websites belonging to the following genres: Sports; Technology; and Entertainment. The objectives of this paper are twofold: (1) to develop a large human-annotated dataset of news in Urdu language for hierarchical text classification; and (2) to classify Urdu news hierarchically using our proposed model based on LSTM mechanism named as Hierarchical Multi-layer LSTMs (HMLSTM). Our model consists of two modules: Text Representing Layer, for obtaining text representation in which we use Word2vec embedding to transform the words to vector and Urdu Hierarchical LSTM Layer (UHLSTML) an end-to-end fully connected deep LSTMs network to perform automatic feature learning, we train one LSTM layer for each level of the class hierarchy. We have performed extensive experiments on our self created dataset named as Urdu News Dataset for Hierarchical Text Classification (UNDHTC). The result shows that our proposed method is very effective for hierarchical text classification and it outperforms baseline methods significantly and also achieved good results as compare to deep neural model.
Leachate and gas production under controlled moisture conditions

Estudio para determinar los efectos sobre el comportamiento de rellenos sanitarios producidos por una tasa variable de infiltracion, adicion de compuestos moderadores del pH, mojado previo de los residuos, temperaturas ambientales diferentes, y disposicion conjunta con lodos y residuos industriales. Se evaluaron el balance de humedad, la calidad del liquido percolado y del gas obtenido
University of Birmingham Controlling the properties of additively manufactured cellular structures using machine learning approaches

Cellular structures are lightweight-engineered materials that have gained much attention with the development of additive manufacturing technologies. This paper introduces a precise approach to predict the mechanical properties of additively manufactured lattice structures using deep learning approaches. Diamond shaped nodal lattice structures were designed by varying strut length, strut diameter and strut orientation angle. The samples were manufactured using laser powder bed fusion (LPBF) of Ti-64 alloy and subjected to compression testing to measure the ultimate strength, elastic modulus, and specific strength. Machine learning approaches such as shallow neural network (SNN), deep neural network (DNN), and deep learning neural network (DLNN) were developed and compared to the statistical design of experiment (DoE) approach. The trained DLNN model showed the highest performance when compared to DNN, DoE and SNN with a mean percentage error of 5.26%, 14.60%, and 9.39% for the ultimate strength, elastic modulus, and specific strength, respectively. The DLNN model was used to create process maps, and was further validated. The results showed that although deep learning is preferred for big data, the optimised DLNN model outperformed the statistical DoE approach and can be a favourable tool for lattice structure prediction with limited data.
Multimodal Multi-Task Stealth Assessment for Reflection-Enriched Game-Based Learning

Game-based learning environments enable effective and engaging learning experiences that can be dynamically tailored to students. There is growing interest in the role of reflection in supporting student learning in game-based learning environments. By prompting students to periodically stop and reflect on their learning processes, it is possible to gain insight into students’ perceptions of their knowledge and problem-solving progress, which can in turn inform adaptive scaffolding to improve student learning outcomes. Given the positive relationship between student reflection and learning, we investigate the benefits of jointly modeling post-test score and reflection depth using a multimodal, multitask stealth assessment framework. Specifically, we present a gated recurrent unit-based multi-task stealth assessment framework that takes as input multimodal data streams (e.g., game trace logs, pre-test data, natural language responses to in-game reflection prompts) to jointly predict post-test scores and written reflection depth scores. Evaluation results demonstrate that the multimodal multi-task model outperforms single-task neural models that utilize subsets of the modalities, as well as non-neural baselines such as random forest regressors. Our multi-task stealth assessment framework for measuring students’ content knowledge and reflection depth during game-based learning shows significant promise for supporting student learning and improved reflection.
Investigation of the Physical and Mechanical Properties of Glycerin Added PVA Fiber Screened Cement Composites

Fiber, PVA fiber, Cement, Glycerin, Engineered cement composites. Concrete, in its simplest definition, is a building material obtained by combining water, cement and aggregate in certain proportions. The basic chemical components of concrete consist of these three materials. However, it has been an important issue to develop concrete against many effects other than its existing mechanical and physical properties, thanks to its economic and easy accessibility. Fibers that have been added to concrete in order to improve the tensile stresses of concrete since the past have been expanding their range today. PVA fiber reinforced concrete, known as engineered cementitious composite (ECC), improves the low tensile stress of concrete and accordingly provides high toughness. ECC concrete is a material developed with a micromechanical mix design. However, the use of ECC concrete is limited due to its disadvantage in terms of easy accessibility and economy. In this study, the physical and mechanical properties of 1 drop of glycerin added PVA fiber and 2 drops of glycerin added PVA fiber concretes were investigated. The physical and mechanical properties of the samples were investigated by adding 2% PVA 1 drop of glycerin fiber and PVA 2 drops of glycerin fiber into the concrete mix design prepared with the micromechanical design. According to the findings, an increase was observed in water absorption, porosity and electrical resistivity tests compared to the control sample and commercial PVA fiber samples; A decrease was observed in the charpy experiment.
DATA AUGMENTATION ON SKIN LESION IMAGE DATA USING GAN FOR INCREASED CNN PERFORMANCE

1-5Department of Computer Science, All India Shri Shivaji Memorial Society COE, PUNE-1 Savitribai Phule Pune University, Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract The skin lesion is abnormal growth or damage of skin cells. There are 7 different types of skin lesions out of which only 4 classes are cancerous. For a better treatment of skin lesions, it is important to detect them in an early stage. For this task, we take the help of automated classification of skin lesions. As of now, Deep learning is providing the best solution possible to the classification of skin lesions.
PALLIATIVE EFFECT OF Re-186 HEDP IN DIFFERENT CANCER PATIENTS WITH BONE METASTASES

The clinical picture of bone metastases is manifested by pain and loss of mechanical stability. Standard treatment options for bone metastases include external beam radiotherapy and the use of analgesics. Due to a large number of lesions in many patients, the use of radionuclide therapy with beta emitters may be preferable. Re-186 hydroxyethydilene diphosphonate (Re-186 HEDP) is one of the radiopharmaceuticals suitable for palliative treatment of metastatic bone pain. The aim of this study was to investigate palliative and side effects of Re-186 HEDP in pts with different type of cancers. Material&method: Thirty one (17 male, 14 female) patients with cancer (10 prostate, 10 breast, 4 rectum, 5 lung, 2 nasopharynx) and bone metastases were included in the study. Therapy was started with a fixed dose of 1295 MBq of Re-186 HEDP. If necessary, the same dose was repeated at least 3 times after an interval of 10-12 weeks A total of 40 standard doses (1295 MBq Re HEDP, Mallinckrodt, Holland) were given; 6 pts received repeated doses (3 doses in 3 pts, 2 doses in 3 pts). The pts with bone marrow suppression were excluded from the study. The pain relief was assessed with ECOG and Karnofsky status index. All pts were evaluated with standard evaluation forms filled daily a maximum of 10 weeks. Results: The respond rate was found as 87.5% in pts with breast and prostate Ca, 75% in pts with rectum Ca, 50% in pts with nasopharynx Ca and 20% in pts with lung Ca. The overall response rate was 67.5%. The palliation period varied between 6 to 10 weeks. The mean palliation period was 8.1 ± 1.3 weeks. Maximal palliation effect was observed between the 3 and the 7 weeks. Any serious side effects were not seen except mild haematologic toxicity. Discussion & conclusion: It is concluded that Re-186 HEDP is a highly effective agent in the palliaton of metastatic bone pain in pts with prostate, breast, rectum cancer, mildly effective in pts with nasopharynx cancer, but not effective in lung cancer. On the other hand, Re-186 seems to be a good alternative to Sr-89 because of its preferable physical characteristics (as short half life and gamma energy emission), low side effect profile, early response and repeated repeatebility.
Meta-learning for automatic model selection and hyperparameter optimisation

Model selection and hyperparameter optimisation is an important step in creating well-performing machine learning models. Optimizing hyperparameters for a given task can be enhanced with information from previous tasks – metalearning. This thesis looks into combining meta-learning with algorithms for hyperparameter optimisation and model selection. We look at several state-ofthe-art hyperparameter optimisation and meta-learning methods. We assess the change in performance for 3 of the reviewed hyperparameter optimisation algorithms when incorporating meta-learning. The results does not prove any significant improvement of meta-learning compared to vanilla model selection methods. The subsequent analysis implies that this is due to the capability of hyperparameter optimisation and model selection methods to find wellperforming configurations on their own in the given time frame.
Evolutionary Artificial Intelligence in Video Games

Evolutionary algorithms are becoming increasingly useful in numerous applications as computational resources improve with time. As computational resources have increased, demand for better quality in all aspects of video games has likewise increased. In this paper we will provide an overview of evolutionary algorithms, with particular attention to the context of video game artificial intelligence. Then we will discuss a specific type of artificial intelligence, often created by evolutionary algorithms, called an artificial neural network. Finally, we will present a number of examples in which evolutionary algorithms have been applied to video game artificial intelligence, as well as the strengths and weaknesses of evolutionary algorithms in this context.
Willingness to receive COVID-19 vaccine in Jordan: A cross-sectional study

Vaccines are considered one of the most outstanding inventions to stop the increase of the COVID-19 pandemic. With availability of vaccine much less is known about public acceptability in Jordan. This study aimed of this study was to determine COVID-19 vaccination acceptance among people in Jordan. A web-based cross-sectional survey was conducted using snowball sampling strategy. The health belief model (HBM) was used to assess predictors of the willingness to accept receiving the COVID-19 vaccine. The result revealed that a total of 2388 respondents, 1303 (51%) were accepted to receive the COVID-19 vaccine. Willingness to accept vaccination relatively higher among females (53%), with higher education (54.6), living in urban area (58.8%), and from center region (60%). three variables under HBM namely perceived susceptibility, perceived benefit, and perceived barriers explained 0.034, 0.469, and 0.495 respectively of variance in acceptance of the vaccine at P&lt; 0.001. in a conclusion the findings reveal the utility of HBM variables in understanding COVID-19 vaccination. It is essential to improve health promotion and reduce the barriers to COVID-19 vaccination.