Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature selection using ModifiedBoostARoota and prediction of heart diseases using Gradient Boosting algorithms

Techniques in Machine Learning can be employed to detect cardiovascular disease at early stages thereby preventing deaths caused by the disease. Identifying the features that significantly participate in the target variable prediction using feature selection methods would help in achieving better accuracy and reducing the computational cost of a model. This paper portrays a new algorithm, ModifiedBoostARoota, developed similar to BoostARoota, differing in the feature elimination process. Also, by choosing XGBoost and catboost as base models in both BoostARoota and ModifiedBoostARoota, a comparison of both the algorithms’ performances are done. ModifiedBoostARoota algorithm has faster performance compared to BoostARoota, when catboost is chosen as the base model. Also, the XGBoost and CatBoost classifiers modelled on features selected by ModifiedBoostARoota gave better accuracy than that of BoostARoota.
Wi-Fi Based User Identification Using In-Air Handwritten Signature

This paper conducts a feasibility study regarding the use of the Wi-Fi channel state information for user recognition based on in-air handwritten signatures. A novel system for identity recognition is thus proposed to observe for distinctive signal distortions along the propagation path for different users. The system capitalizes on the vast availability of Wi-Fi signals for signal analysis without needing additional hardware infra-structure. Since the patterns of the raw Wi-Fi signals are sensitive to the signer’s location, a transfer learning has been adopted to cope with the positional variation. Specifically, features trained at one position are transferred to classify signals collected at another position via a single shot retraining. A kernel and range space projection has been adopted for the single shot retraining. Our experiments show encouraging results for the proposed system.
A Review on Reinforcement Learning based News Recommendation Systems and its challenges

Recommendation systems are helpful in both business perspective and user day to day life. These days online contents are generated in huge amount and due to this, users need a special recommendation application namely personalized News Recommendation, and it is highly challenging due to its dynamic nature. Therefore, getting a suitable and relevant news article for a user is difficult task. To address the above challenge Reinforcement Learning algorithms plays crucial role because these algorithms very much helpful in dealing with the dynamic environment and large space. This paper reviews the different Reinforcement algorithms namely Deep Q-learning network (DQN), Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3) to develop the news recommendation system and also mentioned the challenges faced by the reinforcement recommendation systems. In this study it was found that TD3 is best suited to develop the news recommendation system.
Thermal Noise Measurement and Characterization for Modern Semiconductor Devices

Thermal noise in electronic devices sets the fund amental limitation for receiver front ends [1], analog [2], mixed-signal [3], and even digital circuits [4] for their signal-to-noise ratio (SNR) [5], dynamic range, data acquisition rate, and power consumption. Therefore, thermal noise becomes particularly important for emerging applications such as quantum computing (QC) [6] for its qubits' sensitivity and weak signal strength. It is expected that thermal noise will become a hindrance to these emerging applications due to thermal-noise-induced false bit-flips [7]. Therefore, it is crucial to characterize the thermal noise and understand its physical origin in nanoscale devices. These provide insights for material scientists, process engineers, and circuit designers to optimize their devices and circuits' noise performance so that the social and economic advantages offered by the advances in semiconductor technologies can continue.
Hyperspectral Image Classification using Minimum Noise Fraction and Random Forest

Remote sensing technology is improving day by day which has also increased the uses of hyperspectral imaging tremendously. Exact classification of ground features from hyperspectral images is an important and a popular research area and also have attracted a widespread of attention. A good classification results are achieved by many methods for the classification of hyperspectral imaging. This paper focuses on classification of hyperspectral image using different machine learning techniques like support vector machine (SVM), random forest (RF), polynomial logistic regression (LR), K-Nearest Neighbour (KNN) and decision tree (DT). Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) have been used to reduce the unnecessary and noisy bands present in the dataset.
A Multi-Step Anomaly Detection Strategy Based on Robust Distances for the Steel Industry

Steel making industries exhibit extreme working conditions characterized by high temperature, pressure, and production speed as well as intense throughput. Due to high economic and energy investments of the overall production process, an intense and expensive preventive maintenance program is adopted to avoid breakdowns. Steel making process would greatly benefit from a predictive maintenance module able to detect incoming faults from data process analysis. However, due to intense preventive maintenance, available data recording process operations enclose only a few samples of fault events, avoiding the efficient application of classical data driven anomaly detection models. In an attempt to overcome the above mentioned limits, we report the outcome of an industrial research project on data-driven anomaly detection in a steel making production process. The study assesses a fault detection strategy for rotating machines in the hot rolling mill line: we developed an automatic two-step strategy, which combines two statistical methods over the available data set: more precisely, the combination of Re-weighted Minimum Covariance Determinant estimator and Hidden Markov Models helped identify working conditions in a drive reducer of a hot steel rolling mill line and automatically isolate signs of decreasing performance or upcoming failures. The proposed strategy has been validated on real data collected in a steel making plant placed in the South of Italy.
Empirical mode decomposition for automatic artifact elimination in electrogastrogram

We propose a novel automatic procedure for noise cancellation from the electrical signal recorded from stomach smooth muscles – electrogastrogram (EGG). The presented method is based on the application of empirical mode decomposition (EMD) in the time domain for EGG fragmentation into intrinsic mode functions (IMFs) which is followed by application of criterion for automatic selection of noise-free IMFs. Then, the resulting noise-free EGG signal was composed of IMFs originating solely from EGG. The algorithm is tested on the semi-synthetic dataset generated from the recorded noise-free EGG signal and additive synthetic noise. Our results were presented in relation to signal-to-noise ration. We showed that EMD-based automatic method for noise cancellation is promising for EGG analysis and possibly applicable for analysis of signals with relatively narrow occupied bandwidth in the frequency domain.
Market Analysis of MEC-Assisted Beyond 5G Ecosystem

The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators’ decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players’ revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.
A Critical Study of the relation of Emotion and Stress from the Suprasegmental Features of an Assamese Emotional Spontaneous Speech

the present study is an attempt to explore the real-time applications of suprasegmental (prosodic) features in psychological intervention. The main purpose of this research is to explore different emerging speech analyzing tool for assessing stress. Earlier research work on medical science has shown that speech features can be used as an indicator of stress severity including anxiety disorders, depression, chronic pain, heart disease, asthma, autoimmune diseases, and neurodegenerative disorders. The research work is carried out on simulated controlled Assamese speech rather than natural conversation. It is investigated and observed from the speech signal that the prosodic features are the best indicators for accessing stress in therapeutic intervention to understand the mental state of a speaker. The variation in the supra-segmental speech features pitch, duration, and energy are computed from the emotional utterance to study the psychological condition of the speaker. These parameters are further extracted separately from different levels such as entire utterances, words, and syllables. This analytical non-invasive study is a small contribution in the area of speech processing in medical science.
Pre-trained Word Embeddings for Malayalam Language: A Review

Word embeddings are used to convert human language into a numerical form by encoding the semantic properties of words. Using it each word can be transformed to a set of N-dimensional vectors. It plays a vital role in processing of linguistic applications like natural language inference, information retrieval, sentiment analysis, etc. The goal of word embedding is to capture the meaning of words in their context. And it also find the semantic relationships and similarities between words. The aim of this work is to summarize the existing embedding techniques for words and available corpus for Malayalam language. Since Malayalam is a resource-constrained Indian language, this paper is expected to help NLP researchers in Malayalam to identify the existing resources and to improve the current research trend.
EXOTIC - A Discreet User-Based 5 DoF Upper-Limb Exoskeleton for Individuals with Tetraplegia

Complete, high spinal cord injuries can lead to a condition known as tetraplegia wherein l body is paralyzed from the neck down. Individuals with tetraplegia are greatly limited in their independence and quality of life. Due to the paralysis, these individuals are bound to a wheelchair and require a high level of assistance throughout their everyday. To enable these individuals to regain some of their lost mobility, exoskeletons holds a great potential. Therefore, this work explores the requirements and design choices that went into creating the EXOTIC exoskeleton, an upper-limb exoskeleton designed for individuals with tetraplegia, which enables the user to drink and eat, while maintaining a relatively small form factor. Finally, the available workspace is simulated and visualized, and a pilot test of the basic functionality shows that picking up an item and transferring it to the mouth takes 41 seconds on average.
A New Scheme of Medical Image Fusion Using Deep Convolutional Neural Network and Local Energy Pixel Domain

In this paper, a new multimodal medical image fusion method based on deep convolutional neural networks (CNN) and local spatial domain modification is proposed. First, the source image is fed to Siamese CNN to obtain the weight map and then processed by the Weighted Sum of Eight neighbourhood-based Modified Laplacian (WSEML) to obtain a new image-based WSEML. Next, CT and MRI input images are fed to Weighted Local Energy (WLE). Finally, the activity level measurement based on local energy is dedicated to combining each of the new WLE images and new WSEML images to retrieve useful information at the reconstruction stage. Simulation results demonstrate that the proposed method extracted more useful information with higher visibility from source images, and at the same time reduce fused image artefacts.
A Comparative Study of Heart Sound Signal Classification Based on Temporal, Spectral and Geometric Features

This paper presents a study of murmur recognition in heart sound signal with different machine learning algorithms using combined features. The heart sound signal is required to be pre-processed and segmented first before performing a feature generation and classification process. In this work, two hundred segmented heart cycle samples were parameterized based on temporal, spectral and geometric features as input of cubic Support Vector Machine (SVM), quadratic Discriminant Analysis (DA) and fine K-Nearest Neighbour (KNN) classifiers. The significant of this study is to support the hypothesis in statistic that said higher number of samples provide more accuracy of classifier and smaller margin error. To determine which classifier algorithm is outperformed, cross-validation technique is conducted and varied with different number of samples. The findings indicate cubic Support Vector Machine (SVM) classifier model yields the best performance, achieving an accuracy of 0.97 and F1-score of 0.98 at higher number of samples.
Balance Stability Augmentation for Wheel-Legged Biped Robot Through Arm Acceleration Control

A self-balancing wheel-legged robot provides higher maneuverability and mobility than legged biped robots. For this reason, wheel-legged systems have attracted enormous interest from academia and commercial sectors in recent years. Most of the past works in this field mainly focused on lower body stabilization. Motivated by the human ability to maintain balance in laborious activities by articulating the arm actively, we explore and analyze the active arm control on top of the wheel-legged system to assist in its balance recovery during external pushes and disturbances. This paper presents a control framework to improve the stability and robustness of an underactuated self-balancing wheel-legged robot using its upper limb arm. Furthermore, we use the centroidal moment pivot (CMP) as a key metric to quantitatively evaluate the effect of the active arm usage on the balance stability improvement of the robot in the ROS-Gazebo environment. The difference from the case of nonusage of arm is verified to clarify the impact of the active arm quantitatively. This concept would lead to the wheel-legged biped robot with an active arm for dual purposes, one is for carrying objects, another is for increasing the balance stability. This point is important for future application in a real-world environment with human-robot interactions.
A Detailed Review on Challenges and Imperatives of Various CNN Algorithms in Weed Detection

Weed is one of the main reason for getting less production in agriculture field. At present, farmers are using herbicides to control the weed but it’s having negative impact on crop production. To increase the crop production, farmers want to reduce the usage of herbicides. One of the machine learning algorithm, convolution neural network used to classify the weed and crop with high accuracy. CNN gives the best performance in some critical situations also, such as collected images of different lighting conditions, identification of plant species, overlapped crop with weed and designing an autonomous patch sprayers. This review article presents a brief overview of some significant research efforts in weed detection system by using image processing techniques and convolution neural network in machine learning algorithms. Not only CNN, this article reviewed different successful machine learning techniques of supervised and unsupervised learning approaches such as support vector machines, random forest and artificial neural network. This paper aims at providing different challenges and imperatives of various convolution neural network algorithms used in weed detection. Finally, compared different convolution neural network architectures and finds the best CNN architecture used for weed identification system with respect to accuracy.
A Machine Learning Based Movie Status Evaluation System for Bangladesh Movies

This study develops a machine learning based on movie status evaluation system for Bangladesh movies. The number of movie production rate is growing day by day worldwide and the movie maker invests highly in the movie industry. In such a scenario, this is very important to evaluate movie status. In our proposed research, we evaluate the status of Dhallywood movie (Bangladesh Cinema) based on three different types of Machine Learning (ML) based classification, Binary classifier that includes two targeted classes, Triple classifier that includes three targeted classes and, four classifier that includes four targeted classes. Here, we will give our detailed analysis of data because Bangladeshi movie data collection is the main challenge of our work and consequently, we analyze our data in different ways to set target variable to improve the accuracy of models. For the first time any research focuses on Dhallywood movie data where we have used different machine learning based models for analyzing data. We apply the same ML algorithm for each of the three different class classifications to find which classifier is performing well for our data and the problem by comparing the obtained accuracies. From the experiments, it is observed that the triple class classification accuracy is higher than binary and four class classifications. Among the five applied ML algorithms, the Random Forest shows the best accuracy around 85%. Our research provides a quite different approach to set target variable class based on Wikipedia data, news, actor- actress biography, and viewer response on YouTube for a particular movie. We go for this approach because Bangladeshi movie rating is not perfect on IMDb also the budgets and revenues are not found for all movies.
Role of Wearables in Sports based on Activity recognition and biometric parameters: A Survey

Recently wearables have become a game changer in sports activities. Wearable devices influence the game in tracking and monitoring the performance of the player on field in real time. This paper aims to explore the uses and applications of wearables in the area of sports. These devices help to bridge the gap between technology and user. Also it gives a comprehensive analysis of the player to improve his skills and get real time feedback which would benefit himself and his trainer. This paper surveys the important research work done in the area of wearables under four categories: (i) performance monitoring (ii) sports injury (iii) biometric parameters and (iv) energy efficiency. These various considerations provide many opportunities for advancement in wearable technology with a goal of enabling non-invasive measurement of physiological and activity parameters of a player. Wearable technology is still in development phase and the paper concludes with the research opportunities and the challenges faced.
Use of Oil-in-water Pickering Emulsion Stabilized by Nanoparticles in Combination With Polymer Flood for Enhanced Oil Recovery

Efficient flooding agents are required to produce additional oil from mature reservoir. In this work, oil-in-water Pickering emulsion systems stabilized using nanoparticles, surfactant, and polymer have been developed and tested for enhanced oil recovery with and without a conventional polymer flood. Stability of nanoparticles in the dispersion of surfactant-polymer solution was tested using zeta-potential before use. Several flooding experiments have been conducted using Berea core samples at 13.6 MPa and temperatures of 313 and 353 K. It has been observed that a combination of 0.5 PV polymer flood with 0.5 PV Pickering emulsion was efficient and have resulted in 1–6% additional oil recovery as compared to 0.5 PV Pickering emulsion flooding alone. The injection of polymer flood have shown to enhance the pressure drop in the core sample after emulsion flooding and considered as an important factor for an additional recovery of oil. The effect of temperature on the viscosity of flooding agents in relation to pressure drop and oil recovery have also been investigated. Viscosity and pressure drop of emulsion flood systems have shown to marginally decrease with increase in temperature. Studies on nanoparticle retention using SEM have shown that nanoparticles were retained in the core sample during emulsion flooding which may be detrimental for permeability of core sample. It is observed that Pickering emulsion flood with polymer flood would be effective for the enhanced oil recovery suitable for matured reservoirs.
Review on Image Classification Techniques in Machine Learning for Satellite Imagery

Satellite imagery plays a vital role in research and developments for exploration and improvement in agriculture monitoring and catastrophe monitoring and numerous fields. Satellite images are recorded in superior systems processed by way of computers to extract facts. Machine Learning (ML) is an integral part of artificial intelligence that is used to design a set of rules primarily based on facts.textbf It focuses on the development of software applications that can obtain access to information and use it to instruct themselves. System studying use region tracking via satellite snapshots desires the records for detecting the diverse gadgets. Machine Learning calculations have ended up being a unique asset for examining satellite symbolism of any goal giving better and more nuanced experiences. This article discusses image classification techniques in Machine learning techniques to enhance Satellite Imagery.
Coordinates Determination of Submerged Mobile Sensors for Non-parallel State using Cayley-Menger Determinant

In this paper the problem of localizing submerged sensors is observed and introduces a new method to ordain the coordinates of those sensors which are situated in underwater and they are mobile and only one beacon node is considered for the system. Collecting data of appropriate coordinate of the sensors in underwater wireless sensor networks (UWSN) is essential because the root contains confined value except the knowledge of real data. There is used trilateration technique to detect the submerged sensors and beacon is considered in six different positions. In this proposed model three sensors have been used. To find out the coordinates of the sensor nodes Cayley-Menger determinant is used for the proposed model. Here three mobile sensors and one beacon node that made six different volume tetrahedron shapes by making it in same volume through projection to apply Cayley-Menger determinant. Through enumerating of coordinates of sensors nodes, simulation results make out the proposed mathematical models with minimal errors.