Miracle-dz/UniFAR-FTUs huggingface dataset:
EDAS Based Selection of Machine Learning Algorithm for Diabetes Detection

Diabetes is a matter of concern for the health of the entire world, its diagnosis and cure are among the prime challenge for the medical fraternity, because it can be controlled but can't be cured, sooner the diagnosis the better it will be for the patient. Thus, use of machine learning for timely classification, of diabetes plays a vital role to protect patient from the life threatening complications in future. Various classification techniques are available in Machine Learning (ML) viz. Support Vector Machines (SVM), Random Forest, Naïve Bayes Classifier, Linear Regression (LR), K-Nearest Neighbor(KNN) algorithm, etc. etc. But the question is which of the classification techniques, timely and accurately identifies this sensitive disorder. While predicting Diabetes using any machine learning algorithm, the accuracy, specificity and sensitivity, are some of the important parameters. The strengthening of these parameters requires the understanding of dataset under consideration, i.e. whether the data set is having some missing values or outliers, if missing values exists then to strengthen the prediction accuracy; one has to apply the data imputation techniques on the dataset. In the performed work the well-known dataset (Pima Indian) from UCI repository, was subject to data imputation techniques to handle the missing values present in it(tabulated in Table-1). Thereafter the said Machine Learning techniques were applied, and compared on the basis of various parameters viz. Accuracy, Sensitivity, and Specificity etc., to choose the best among algorithm one has to compare the multiple criteria's altogether, which is quite challenging. Thus, in the performed work the Evaluation Based on Distance from Average Solution (EDAS) is applied, it is a technique of Multi-Criteria-Decision-Making (MCDM). By applying EDAS over the performance evaluation statistics (speed, accuracy, specificity and sensitivity) of various classification algorithms viz. Naïve Bayes (NB) Classifier, Support Vector Machines (SVM),K-Nearest Neighbor (KNN), Random Forest(RF), Linear Regression (LR); it is found that the Naïve Bayes (NB) is Ranked as the best Classifier and Random Forest (RF) was the second best ranked alternative for analyzing the PIMA INDIAN DATASET, to predict the diabetes.
A Combined Approach Using Image Processing and Deep Learning to Detect Pneumonia from Chest X-Ray Image

Pneumonia is an epidemic disease that is needed to be detected in the early stage to prevent unfortunate deaths. Traditional methods take a lot of time to detect the disease. With the introduction of Medical Imaging, the detection of disease has been accelerated by using chest x-ray image. But it also requires the availability of an expert and experienced radiologist in order to interpret the x-ray image accurately. Sometimes, manual interpretation is affected by various kinds of artifact in X-ray or Optical Coherence Tomography(OCT) images. For this reason in our paper, we have proposed a combined approach using Image Processing and either VGG-16 or VGG-19, variants of Deep Convolutional Neural Network for automatic detection of pneumonia from Chest X-ray image. We have used Mendeley OCT and Chest X-Ray dataset to evaluate our model. We have achieved an accuracy of 96.2% using VGG-16 and 95.9% using VGG-19, both of which outperform transfer learned InceptionV3 benchmark model used on this dataset which has an accuracy of 92.8%.
Simultaneous Improvement of Surface Properties of the Liquid Silicone Rubber by Direct Fluorination

In this report, liquid silicone rubber (LSR) was fluorinated and studied. ATR-IR and XPS results clearly indicate the presence of Si-F bonds as well as C-F bonds in the fluorinated layer. The fluorination of the LSR is not a simple substitution of fluorine atoms for hydrogen atoms of methyl groups or for methyl groups, and during the fluorination besides fluorine atoms there are other reactive species, such as methyl derivatives and their fluorides, which would be also attached to Si or graft to methyl groups via hydrogen replacement. The experimental results show that the fluorinated surface is highly hydrophobic and partially conductive. The flashover tests using a sheet sample/finger electrode arrangement found a more than 60% increase in DC flashover voltage of the LSR material by the fluorination. The simultaneous improvement of surface properties is meaningful for outdoor insulation applications of the material.
Image Segmentation Method Based on Fractional Varying-order Differential

Image segmentation plays a very important role in many applications. At present, the most difficult problem of image segmentation is segmenting intensity inhomogeneous images, some hybrid methods have better segmentation results than the traditional methods. In this paper, a new hybrid level set method based on fractional-varying-order differential curvature is proposed. We define a new curve evolution curvature calculated by fractional-varying-order differential according to the gradient of the level set function, so that the diverse order differential can be used in a whole curve evolution at the same time, which can describe the image edge more accurately. The energy functional for the proposed model consists of three terms: local term, length term and penalty term. The evolution of the level set function is the gradient flow that minimizes the overall energy functional. Experimental results for both synthetic and real images show desirable performance of our method. The Dice similarity coefficient are employed as the comparative quantitative measures for the segmented results.
An Entanglement-Inspired Action Selection and Knowledge Sharing Scheme for Cooperative Multi-agent Q-Learning Algorithm used in Robot Navigation

Multi-agent reinforcement learning, especially learning in unknown complex environments, requires new algorithms. In this work, our focus is on adopting the concept of the quantum entanglement phenomena to the action selection procedure of multi-agent Q-learning, aiming to enhance the learning speed, collision avoidance, and also providing full coverage of the environment. The exploration procedure is exclusively induced by a memory-based probabilistic sequential action selection method acting as a knowledge hub, shared among the agents, which is the central pillar of this work. This causes enhancing the parallelism of the learning process, plus, building an effective yet simple communicating-bridge between the learning agents; that is, they can signal and guide one another through sharing their gained experience and knowledge in order not to repeat the same mistake that the other agents have already run into. The simulation results demonstrated the effectiveness of our proposed algorithm in terms of reducing the learning time, significant reduction of collision occurrence, and providing full coverage of big complex clutter environments.
The validity of a dual Azure Kinect-based motion capture system for gait analysis: a preliminary study

The Microsoft Kinect can track human motions in various motor tasks. The recently released Azure Kinect is reported to have an improved image sensing technology. However, the validity of this newest sensor for gait analysis is still unknown. In this study, a dual Azure Kinect-based motion capture system was developed. Gait analysis was conducted with five healthy adults. Joint angles calculated by this system were compared with that acquired by the Vicon motion capture system. The coefficient of multiple correlations (CMC) and root mean square errors (RMSE) were computed. The dual Azure Kinect system could provide accurate knee angles (CMC=0.87±0.06, RMSE=11.9°±3.4°). Hip sagittal angles demonstrated moderate agreement with the reference (CMC=0.60±0.34, RMSE=15.1°±6.5°). The hip frontal, transversal, and ankle angles demonstrated poor validity. Although levels of accuracy for each joint varied, the dual Azure Kinect system demonstrated an overall improved validity than the Kinect V2. Future studies should involve more participants and patient populations, and compare different versions of sensors in the same experimental setup simultaneously to warrant the findings derived from this study. Furthermore, it is also necessary to standardize the experimental setup and involve more sensors to provide adequate depth images for analysis.
Heart Rate Detection with the Off-the-Shelf Camera: Static to Non-Static

Imaging photo-plethysmography (IPPG) technique has been proven to detect heart rate (HR) with a camera. Currently it might be used in a rigid scenario, such as the static subject, the short measurement distance and good illumination. In this paper, we try to relax these constraints by extending the measurement distance and applying it to the non-static subject with IPPG technique using the off-the-shelf camera. To achieve it, the landmark detection method is used to locate the facial region of interest (ROI) for the subject during movements. On this ROI, R/G/B channel signals are extracted with the spatial average of pixels. Then we use a skin appearance model (CHROME) to remove the noise, and HR is estimated from the output of the CHROME through the power spectrum. Experimental results showed that our method works effectively in both static and non-static scenarios. In static scenario, the measurement distance can extend to 4 meters, while the accuracy can reach 86% compared with the results from a finger detection equipment. In non-static scenario, our method achieve better results than previous studies.
Analysis of an Adaptive Fuzzy Control Scheme: Application to a Lower Limb Exoskeleton

Gait training helps in the assistance and rehabilitation of those who have problems with their locomotion system through better control of their limbs and also prevents degeneration of the musculoskeletal system. However, gait tracking should be accurate and the system should be robust for large-scale deployment. In this paper, an adaptive fuzzy control scheme (AFCS) is investigated for generating assisting torque for a lower limb exoskeleton consisting of knee-ankle orthosis. Grey wolf optimization is used for choosing optimum parameters of AFCS. Further, extensive simulation studies for trajectory tracking, parametric uncertainty handling and disturbance rejection analysis were carried out and a comprehensive comparative analysis was performed against a state-of-the-art control technique based on a coupled adaptive frequency oscillator (CAFO) to track a given non-sinusoidal pattern. Simulation results establish that the AFCS outperforms CAFO based control scheme for all cases.
Influência do cálcio sobre o crescimento e solutos em plântulas de sorgo estressadas com cloreto de sódio

Increased Ca2+ concentrations in the root environment have been suggested to minimize salinity effects on plant growth. In this work, seedlings of two forage sorghum genotypes (Sorghum bicolor (L.) Moench), one salt tolerant (CSF 20) and the other salt sensitive (CSF 18), were exposed to two Na+ (0 and 75 mmol L-1) and two Ca2+ (0.65 and 2.60 mmol L-1) levels in nutrient solution, in a 2 x 2 x 2 factorial design. After seven days of salt treatment, the shoot and root dry matter as well as Cl-, Na+, K+, Ca2+, and proline concentration were measured. Salt stress reduced shoot and root dry matter yield in both genotypes, especially in the salt sensitive genotype. An increase in Ca2+ concentration in the nutrient solution partially reduced the salt effects, mainly in the shoots of the salt tolerant genotype. The salt stress also increased leaf and root Na+ and Cl- concentration but reduced that of K+ in both genotypes, especially in the sensitive one. Additionally, a reduction in Ca2+ and an increase in proline concentration were observed, though only in the leaves. The increase in the Ca2+ concentration in solution partially alleviated the salt effects by reducing Na+ and Cl- and increasing K+ contents, especially in the leaves of the salt tolerant genotype. This genotype presented a much lower Na+/K+ ratio than the sensitive one, and this difference between genotypes increased along with the increasing Ca2+ concentration in the nutrient solution. Increased Ca2+ concentrations in the nutrient solution also increased leaf and root Ca2+ contents of both genotypes, but salt stressed seedlings were not able to maintain the same tissue concentrations of these ions of unstressed control seedlings. Calcium had no significant effect on proline accumulation in the plants, either in the absence or presence of salt stress.
Twitter sentiment analysis using deep learning models

Twitter is a massive repository and a gold mine of human thoughts that expresses a person’s instant feeling. A retrospective review of tweets during the ensuing COVID-19 pandemic may provide valuable insights into the understanding of a person’s feelings. With an enormous amount of data, training a model to understand the precise feeling is a daunting job. There are many developments recorded in the field of deep learning to pave the way forward. The purpose of this paper is to compare the tweet sentiment classification using Google BERT, attention based Bidirectional LSTM and Convolutional Neural Networks (CNNs). The final models are trained on the Twitter dataset SemEval-2016, where the embeddings are fine-tuned again. Such models proved to be highly effective and accurate in the study of emotions as opposed to machine learning techniques.
Employing Image Processing Techniques for Hardware Trojans Detection

Hardware Trojans (HT) become an emerging issue in the integrated circuits (IC) production, which have many malicious effects on the ICs such as functionality changes, overheating problems and leaking information. According to the Trojans’ small size and ICs complex structure, detecting Trojans is a serious challenge to solve. In this paper, we propose a novel approach in order to detect very small Trojans with high accuracy. First, we generate the ideal power heatmap of ISCAS benchmarks with calculating switching activities and the dynamic power of each gate and node of the testing circuits. Second, we employ image processing techniques (similarity structure index and thresholding) to detect differences in the heatmaps which caused by Trojans. With our approach, very small Trojans that consume power less than 0.001 of the total power consumption can be detected and located with 97 percent average accuracy.
A deep music genres classification model based on CNN with Squeeze & Excitation Block

With the development of mobile terminals and Internet technology, people have increasingly convenient mediums to obtain digital music. However, complex music genres and massive music libraries have brought great challenges to music information retrieval. Music genres are high-level labels for music information, which would consume a lot of time and resources when manually tagged. This paper proposes a new model: in order to fully mine the latent information hidden in the input spectrum graph, we build a music genre classification system based on the convolutional neural network that includes Squeeze & Excitation Block (SE-Block), and then use Bayesian optimization to search the best parameters of SE-Block. Finally, we choose the GTZAN dataset for experiments and achieved a classification accuracy of 92%, which is significantly better than most previous research work.
DroidTKM: Detection of Trojan Families using the KNN Classifier Based on Manhattan Distance Metric

Currently, the speed of Android malware publications has increased dramatically. The rapid rise of malware has made malware detection and family classification to become an important challenge; because attackers can publish more malware with minor changes in existing android applications. These minor changes in the application lead to the creation of multiple families of malware. So far, many methods have been proposed to detect malware applications and classify them. However, few methods focus on detecting malware families. In this paper, a detection method is proposed to identify Trojan families in order to improve accuracy and reduce error rates. To achieve these purposes, static and dynamic analysis are used to extract the required features of the applications. The k- means method has also been used to preprocess the obtained dataset. Then, a detection model is developed to identify families using the classifiers of K-Nearest Neighbor (KNN), Support Vector Machine, and Iterative Dichotomiser 3. The accuracy of KNN is also measured according to different distance metrics which has not yet been studied among malware detection methods. The proposed method is able to detect a variety of Trojans using KNN based on Manhattan metric with an accuracy of 97.83% and False Positive Rate (FPR) of 0.06%. The comparison between the performance of the proposed method and the other methods shows a 4.83% and 0.94% improvement in terms of accuracy and FPR, respectively.
An Integrated Detection and Treatment Recommendation Framework for Breast Cancer Using Convolutional Neural Networks and TOPSIS

Breast cancer is the most prevalent type of cancer, accounting for 14 percent of all cancers among women in India, according to National Health Portal. Various machine learning including deep learning methods find extensive applications in the field of medicine i.e. Computer Aided Diagnosis (CAD) and one of its major examples is detection of cancerous cells. While many such systems explore the idea of classifying the region of abnormality as benign or malign, traditional treatment recommendation solely depends on the knowledge of the physician examining. This paper explores regression using Convolutional Neural Networks (CNN) to pin-point the probable abnormal region in mammograms and calculate several morphological, texture and histogram features associated with it. These features are then leveraged to recommend the next probable treatments. The recommendation is based on the extracted features and therefore constitutes a Multi-Criteria Decision Making (MCDM) problem. To implement this type of decision making rightfully, the paper makes use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) where the identified features are used as criteria and experts’ opinion are used as alternatives to obtain ranked recommendations. The methodology proposed in the paper is capable of recommending the correct treatment with an accuracy of 81.5%. The proposed methodology would make treatment recommendation reachable even at the most remote places where advanced facilities including consultation with specialists are not easily available.
Automatic License Plate Recognition of Bikers with No Helmets

Bikers who do not wear helmets pose a major threat to themselves. A UN Motorcycle study done in 2017 showed that around fifteen thousand deaths of bike riders could be avoided annually in India with safety measures. It also mentions that wearing helmets improves the chances of surviving accidents by 42 % and helps riders to avoid injuries by 69 %. Due to large vehicular traffic, manual monitoring of vehicles has become a problem. This can be automated using Deep Learning, which is the purpose of our work.Our tool, with the help of feed from a camera, monitors traffic and detects riders who are not wearing helmets. It then notes down the license plate data of the vehicle in a database, thus, making it easier for officials to track and warn the riders who are violating traffic rules.
Combustion of premixed methane and air in ducts

Extinction limits are reported for flames stabilized on sudden expansions in plane and round ducts, with acoustically open and closed upstream ends, with and without downstream nozzles and, in the case of the round duct, with added swirl. Two main regimes of combustion oscillations were observed, one close to stoichiometry and the other close to the limits, and details of the corresponding flame structures were obtained from simultaneous measurements of pressure and imaging of the chemiluminescence of the CH radical. Similar results are presented for disc-stabilized flames. The pressure measurements in the unconstricted sudden-expansion flows show that combustion oscillations with equivalence ratios close to unity were associated with longitudinal acoustic frequencies, and the chemiluminescence images show the roll-up of vortices close to the pressure maximum followed by their growth and convection. With an open upstream end, the dominant acoustic mode was a half wave in the entire duct, and it changed to a quarter or three-quarter wave when the upstream end was acoustically closed. The addition of a downstream nozzle introduced a bulk-mode oscillation that dominated in the round duct but the large-amplitude oscillations were still associated with a longitudinal mode in the plane geometry: the quarter wave in the upstream duct this time. The pressure signals showed low-frequency modulations that increased with flow rate and, therefore, with heat release, and these were associated with flame movements caused by high strain rates. As the equivalence ratio was reduced toward the lean-extinction limit, the pressure signals fluctuated with periods of the order of 100 ms and the chemiluminescence showed that these were associated with cyclic longitudinal movements of the flame. These movements were caused by local extinction due to the high strain rates in the shear layer close to the stabilizer, with subsequent downstream translation of the flame until the strain rate reduced sufficiently to allow the flame to re-establish and return through the region of recirculation. The disc-stabilized flames gave rise to similar oscillations prior to extinction. Constricting the ducts with an exit nozzle narrowed the flammability range by ca. 30%, increased the amplitudes of the nearlimit oscillations by two orders of magnitude, and led to higher emissions of NOx. Attempts to suppress the near-limit oscillations in the sudden-expansion flows by imposing oscillations on the pressure field are described and reduced the amplitudes in the constricted ducts by up to 70% through suppression of the bulk-mode oscillation. They were less successful with near-stoichiometric oscillations and high flow rates, where the modulations presented problems that were not solved by active or open-loop control. Possible implications of the results for the operation of gas turbines are considered.
Novel Response Acquisition Method for Enhancing Spatial Resolution in Capacitive Tactile Sensing Array

In this study, a novel response acquisition method was proposed and verified in a capacitive tactile sensing array, which collected signals from shared capacitors between two adjacent sensing units. In the proposed design, the electrodes of a capacitor exhibited intentional misalignments in the xy-plane and four types of misalignments existed in the array. The tactile sensing array was then numerically evaluated, fabricated, and demonstrated. With the proposed method, the previously undetectable normal force was resolvable with noticeable (at least 225%) spatial resolution enhancement. Because the composition of the sensing array, number of capacitors in the array, and electrode dimensions in the capacitors were all unaltered from the conventional design, high detection sensitivities (on average 3.261 pF/ N for the normal force) were obtained. Additionally, although the four types of sensing unit required four corresponding algorithms to derive the shear angles, results proved that the supportiveness was remained and accuracy was unaltered compared to existing similar works.