Miracle-dz/UniFAR-FTUs huggingface dataset:
An Approach Towards Automatic Detection of Toxoplasmosis using Fundus Images

Ocular Toxoplasmosis (OT) is a widespread infectious chorioretinal disease whose timely diagnosis and treatment are crucial to prevent potential vision loss. Diagnosing OT is a challenging task ranging from tedious analyses of fundus images of the eye to serological clinical tests. An automated approach using convolutional neural networks (CNNs) towards diagnosing OT by analyzing fundus images is described. Fundus images are segmented to patches using a sliding window and are classified into healthy and unhealthy fundus image patches using a CNN model. An OT lesion heat map of a fundus image is generated from these patches. The heat map and patch features are then combined to develop a dual input hybrid CNN model detecting OT fundus images with high accuracy. The approach was applied to a dataset of fundus images involving OT and normal subjects and was highly effective in identifying fundus images having OT lesions.
A Spectral Convolutional Net for Co-Optimization of Integrated Voltage Regulators and Embedded Inductors

Integrated voltage regulators (IVR) with embedded inductors is an emerging technology that provides point-of-load voltage regulation to high-performance systems. Conventional two-step approaches to the design of IVRs can suffer from suboptimal design as the optimal inductor depends on the characteristics of the buck converter (BC). Furthermore, inductor-level trade-offs such as AC and DC resistance, inductance and area can not be determined independently from the BC. This co-dependency of the BC and the inductor creates a highly non-linear response surface, which raises the necessity of co-optimization, involving multiple time-consuming electromagnetics (EM) simulations. In this paper, we propose a machine learning based optimization methodology that eliminates EM simulations from the optimization loop to significantly reduce the optimization complexity. A novel technique named as Spectral Transposed Convolutional Neural Network (S-TCNN) is presented to derive an accurate predictive model of the inductor frequency response using a small amount of training data. The derived S-TCNN is then used along with a time-domain model of the BC to perform multi-objective optimization that approximates the Pareto front for 5 objectives, namely inductor area, BC settling time, voltage conversion efficiency, droop and ripple. The resulting methodology provides multiple Pareto optimal inductors in an efficient and fully automated fashion, thereby allows to rapidly determine the optimal trade-offs for possibly contradicting design objectives. We demonstrate the proposed framework on co-optimization of solenoidal inductor with magnetic core and BC that are integrated on silicon interposer.
Recognition of Breathing Activity and Medication Adherence using LSTM Neural Networks

Obstructive inflammatory pulmonary diseases are life-long conditions of the airways affecting millions worldwide. A crucial step towards effective self-management is the adherence of the patients to their medication. Accurate detection of pressurised metered dose inhaler audio events can significantly improve medication adherence facilitating more meaningful interventions by medical personnel. Towards this direction, this work presents a data-driven approach for monitoring pressurised metered dose inhaler medication adherence employing recurrent neural networks with long short term memory (LSTM) units with spectrogram features. Evaluation studies took place with intra-subject and inter-subject settings, demonstrating that our LSTM-based approach yields higher accuracy compared to classical machine learning methods reaching a prediction accuracy ranging from 92% to 94% taking into account also samples comprised of a mixture of patterns belonging to more than one of the predefined classes.
Virtual Reality Movement Therapy for Post-Stroke Upper limb Rehabilitation Trial

Upper limb impairment is common among stroke survivors and has a severe impact on independence in everyday activities. Recent evidence indicates that proper rehabilitation that focuses on high-intensity motivating exercises may drive neural repair and/or restoration. Virtual reality game-based rehabilitation system creates a stimulating and enjoyable task-oriented environment where feedback and exercise intensity can be adjusted to provide individualized rehabilitation programs and improve functional recovery. The aim of this study is to examine the effect of combining both game-based virtual reality movement rehabilitation and conventional rehabilitation on functional activities of the affected upper extremity. 10 stroke patients (sub-acute stage) participated in a 5-week training program (2 days per week). Duration of each session was 60 minutes, the first 30 minutes were dedicated to conventional treatment and the last 30 minutes to the video game play using the Nintendo Wii. Significant improvement in all outcome measures was found after the intervention. Our findings support the clinical effectiveness of game-based virtual reality movement rehabilitation on upper limb functional recovery in sub-acute stroke survivors.
Factors Influencing Metal Binding Efficiency at Solid/Liquid Interface: An Investigation for the Prediction of Heavy Metal Ion Sensing Performance

This work investigated the factors that may influence metal binding efficiency of the chelating agent-modified microfiber. The outcome of this experimental study shows the sensing behavior of a chelating agent-modified microfiber is affected by the denticity of the chelating agent, ionic radii and hardness level of the metal ion.
Plant Leaf Detection and Disease Recognition using Deep Learning

The latest improvements in computer vision formulated through deep learning have paved the method for how to detect and diagnose diseases in plants by using a camera to capture images as basis for recognizing several types of plant diseases. This study provides an efficient solution for detecting multiple diseases in several plant varieties. The system was designed to detect and recognize several plant varieties specifically apple, corn, grapes, potato, sugarcane, and tomato. The system can also detect several diseases of plants. Comprised of 35,000 images of healthy plant leaves and infected with the diseases, the researchers were able to train deep learning models to detect and recognize plant diseases and the absence these of diseases. The trained model has achieved an accuracy rate of 96.5% and the system was able to register up to 100% accuracy in detecting and recognizing the plant variety and the type of diseases the plant was infected.
Bio-inspired energy efficient clustering approach for wireless sensor networks

In this paper, we proposed an approach to clustering based on bio-inspired behaviour and distributed energy efficient model. The motivation to propose this clustering approach is due to the challenge of performance in terms of finding an efficient way to send data packets to base stations and to maintain the lifetime performance of wireless sensor networks. The bioinspired approach adopted the behaviour of a bird called Kestrel. This behaviour is expressed using mathematical formulation and then translated into an algorithm. The bio-inspired algorithm is combined with the distributed energy efficient model for clustering to ensure efficient energy optimization. The proposed clustering approach, referred to as DEEC-KSA, is evaluated through simulation and compared with benchmarked clustering algorithms. The result of simulation showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime and network throughput. Additionally, the proposed DEEC-KSA has the optimal time (in seconds) to send packets to base station successfully.
Salvianolic acid B inhibits matrix metalloproteinases by NF-κB signaling in rabbit chondrocytes

This study investigates the chondroprotective properties of Salvianolic acid B (Sal B) in interleukin (IL)1β-induced rabbit chondrocytes. Quantitative real-time PCR, enzyme-linked immunosorbent assay (ELISA) and western blot were performed to investigate the effects of Sal B on the mRAN and protein levels of matrix metalloproteinases (MMP)-1, MMP-3, MMP-13, tissue inhibitors of metalloproteinase-1 (TIMP-1) and collagen II in IL-1β-induced rabbit chondrocytes. Sal B inhibits the mRNA level as well as the protein level of these MMPs. Sal B also increases IL-1β-inhibited mRNA and protein levels of TIMP-1 and collagen II. In addition, Sal B inhibits the IL-1β-induced activation of nuclear factor kappa B (NF-κB) and the degradation of inhibitor of κB (IκB)-α. Our data suggest that Sal B exhibit chondroprotective properties in chondrocytes.
Improvement in Deep Networks for Optimization Using eXplainable Artificial Intelligence

With the recent advancements in the field of explainable artificial intelligence (XAI), which covers the domain of turning deep network architectures from black boxes to comprehensible structures, it became easy to understand what goes on inside a network when it predicts an output. Many researchers have successfully shown the ‘thought process’ behind a network’s decision making. This rich and interesting information has not been utilized beyond the scope of visualizations once the training finish. In this work, a novel idea to utilize this insight to the network as a training parameter is proposed. Layer-wise Relevance Propagation (LRP), which obtains the effect of each neuron towards the output of the whole network, is used as a parameter, along with learning rate and network weights, to optimize the training. Various intuitive formulations have been proposed, and the results of the experiments on MNIST and CIFAR-10 datasets have been reported in this paper. Our proposed methodologies show better or comparable performances against conventional optimization algorithms. This would open a new dimension of research to explore the possibility of using XAI in optimizing the training of neural networks.
SRAM Issues and Scrutiny at 45nm having Low Leakage Power and Leakage Current

In this paper an overview of conventional 6T SRAM and its limitations are discussed. Then a brief discourse for different design issues in a 6T static random access memory (SRAM) that is scathing element in SoC and VLSI applications. The cell is reported with high SNM 398.67mV, leakage power of 112.49nW, leakage current of 3. 48μA and delay 20.77fs. This paper shows SRAM stability which is scrutinized by process variations and leakage issues for cell augmentation. Read and write stability is analyzed; results are obtained and verified using 45nm technology. The originality and contribution of this paper is in its high SNM, RSNM and WSNM (measured using butterfly approach) with low leakage power and current. The previous results have lower WSNM by 8.44%, Tox variation of 27.08%, the SNM is 360mV, leakage power 232. 2μW and high delay 8.57ps disrupting cell stability. The post layout simulations and measurements proof the effectiveness of designed cell.
Application model of tasks scheduling problem in distributed systems solving problems of road traffic planning in smart cities

In this work, we present a new model to improve city traffic with the aim of reducing congestion and travel time. This model is inspired by the process used in task scheduling and load balancing problem in distributed systems to reduce makespan based on optimization methods such as ACO. The makespan in this case of application corresponds to the time wasted in traffic jams. The results obtained are satisfactory and confirm that it is necessary to optimize traffic congestion problems in smart cities in order to regulate the traffic flow and to inform drivers in real-time on the associated routes in order to fight congestion, energy waste and therefore generate less pollution.
Churn Prediction in Banking System using K-Means, LOF, and CBLOF

Customer churn prediction helps in identifying those customers who are probable to stop a subscriptions, products or services, and is therefore very essential for any business. Churn predictions can be very valuable for customers retentions, as it helps in predicting customer that are at risks of sendoff. It is more challenging to put forth churn prediction in banking sector, as there are no contractual agreements between a customer and a bank regarding the duration of services. Loss of customers can be very costly as it is very expensive to obtain new customers in this age of competition. There are many churn prediction techniques however; K-Means, Local Outlier Factors (LOF) and Cluster-Based Local Outlier Factors (CBLOF) have not been used so far for this purpose. In this research, we apply these techniques for customer churn prediction. The results are evaluated and analyzed using Precision (Pr), Recall (Re) and F1-Measures to justify the efficiency and effectiveness of this research.
Enabling Grant-Free Access with Non-orthogonal Preambles and Multi-codebook SCMA

Grant-free random access is one of the key elements in the emerging mobile system in which a low-latency communication requirement must be met to support a massive number of machine-type communication (mMTC) devices. The random access success rate of sparse code multiple access (SCMA)-based grant-free (GF) access is mainly determined by factors such as collision in random access resource selection, users’ activity, and data misdetection. At the same time, it is challenging to decrease the effect of these factors while reducing signaling overhead. One approach to handle the challenge is to employ a large set of non-orthogonal preambles for active user detection and channel estimation while allowing users’ data to be differentiated by their channel even if the same SCMA codebook (CB) is selected. In this paper, we introduce a new approach that maps a user’s consecutive symbols to code words from different SCMA CBs in around-robin manner, as opposed to conventional SCMA in which a single codebook (CB) is employed per a user’s random access transmission. It is also shown that the proposed multi-codebook SCMA-based GF access scheme outperforms the conventional single-codebook SCMA-based one by overloading of more users(up to 200%) while keeping the same preamble length, i.e., the same level of overhead.
A Novel Deep Learning Approach for Left Ventricle Automatic Segmentation in Cardiac Cine MR

Cardiac magnetic resonance imaging provides a way for heart’s functional analysis. Through segmentation of the left ventricle from cardiac cine images, physiological parameters can be obtained. However, manual segmentation of the left ventricle requires significant time and effort. Therefore, automated segmentation of the left ventricle is the desired and practical alternative. This paper introduces a novel framework for the automated segmentation of the epi- and endo-cardial walls of the left ventricle, directly from the cardiac images using a fully convolutional neural network similar to the U-net. There is an acute class imbalance in cardiac images because left ventricle tissues comprise a very small proportion of the images. This imbalance negatively affects the learning process of the network by making it biased toward the majority class. To overcome the class imbalance problem, we propose a novel loss function into our framework, instead of the traditional binary cross entropy loss that causes learning bias in the model. Our new loss maximizes the overall accuracy while penalizing the learning bias caused by binary cross entropy. Our method obtained promising segmentation accuracies for the epi- and endo-cardial walls (Dice 0.94 and 0.96, respectively) compared with the traditional loss (Dice 0.89 and 0.87, respectively)
Obstacle-Aware Group-Based Length-Matching Routing for Pre-Assignment Area-I/O Flip-Chip Designs

A robust redistribution layer (RDL) router is required for advanced package designs, where the length-matching constraint for a group of nets needs to be considered to preserve good timing properties at the package level. For area-I/O flip-chip design with pre-assigned nets on RDLs, we propose the first group-based length-matching routing framework that can simultaneously minimize the wirelengths of an arbitrary group of nets with and without equal-length constraints, based on an equal-length-aware A*-search algorithm and a bounded sliceline grid (BSG) snaking one. For the irregular structure of the area-I/O flip-chip design, we apply Delaunay triangulation and Voronoi diagram to model the routing resources more precisely. To effectively consider the equal-length constraints in the earlier stage, we first profile the routing resource to obtain an approximation of the longest net, and then adopt the equal-length-aware A*-search algorithm to extend shorter nets to match the estimated longest net. A BSG-based snaking method is then applied to meet the equal-length constraint, while preserving the minimized wirelength of unconstrained nets. Experimental results demonstrate that our framework can solve all benchmarks effectively and efficiently.
A Signal Processing Method for Artefact Rejection in Transcranial Doppler Signals used for Micro-embolus detection

Atefacts are classified as High Intensity Transient Signals appearing in the Doppler signal, during TCD recording. Artefacts could be caused by probe tapping or displacement, movement of the patient as well as various external factors. In Holter TCD, the number of artefacts are further increased due to the servo-controlled probe positioning and long recording time. Current artefact rejection methods must be adapted to the new holter devices. Therefore, in this paper we present a complete procedure for eliminating two types of artefacts that occur during Holter TCD. The latter two types are bidirectional areftact, occurring in the energy of both positive and negative frequencies, and unidirectional artefacts occuring only in the energy of the negative frequencies. From a dataset of 25 signals, 218 artefact signatures were identified; 95% of which are bidirectional and 5% unidirectional. As a final result, 98% of the artefacts where successfully removed.
Synthesis of ZnO Nanorod using Hydrothermal Technique for Temperature Sensor

Solid-state based temperature sensor is gaining attention due to its flexibility in design and high accuracy. ZnO is a semiconductor material which is inexpensive and suitable to be used in a harsh environment such as in dangerous radiation and high temperature. ZnO nanorods are synthesized using hydrothermal technique with three different reaction times, i.e. 20, 30 and 40 hours. The average areas of ZnO nanorods produced decreased with increasing of reaction times. The nonuniformity of ZnO nanorods grown is decreased from 32% to 31.25% with increasing reaction times. The I-V characteristics are measured with different temperatures from 27°C to 300°C. As the temperature increased, the resistance of the nanorod device decreased. The best sensitivity towards temperature achieved with nanorods produced for 20-hours reaction times which is 1.72 Ω/°C.
Insider Threat Identification Using the Simultaneous Neural Learning of Multi-Source Logs

Insider threat detection has drawn increasing attention in recent years. In order to capture a malicious insider’s digital footprints that occur scatteredly across a wide range of audit data sources over a long period of time, existing approaches often leverage a scoring mechanism to orchestrate alerts generated from multiple sub-detectors, or require domain knowledge-based feature engineering to conduct a one-off analysis across multiple types of data. These approaches result in a high deployment complexity and incur additional costs for engaging security experts. In this paper, we present a novel approach that works with a variety of security logs. The security logs are transformed into texts in the same format and then arranged as a corpus. Using the model trained by Word2vec with the corpus, we are enabled to approximate the posterior probabilities for insider behaviours. Accordingly, we label the transformed events as suspicious if their behavioural probabilities are smaller than a given threshold, and a user is labelled as malicious if he/she is associated with multiple suspicious events. The experiments are undertaken with the Carnegie Mellon University (CMU) CERT Programs insider threat database v6.2, which not only demonstrate that the proposed approach is effective and scalable in practical applications but also provide a guidance for tuning the parameters and thresholds.
The Impact of Web Visual Aesthetics on Purchase Intention

The web design should not only deliver the basic merchandise trading, but also embellish the web site in order to enhance the purchase intention through the concept of visual aesthetics, color, pictures and films. Based on the theory Stimulus-Organism-Response(S-O-R), the model of aesthetic experiences, and two constructs of web visual aesthetics “aesthetic formality and aesthetic appeal”, this study establishes a reaction mode of web visual aesthetics perception. This study investigates the effect of user’s "web visual aesthetics perception" through "positive emotions", "aesthetic experience value" and "satisfaction" on "purchase intention". The software AMOS 20 is used to conduct a structural equation modeling analysis of the 288 respondents collected from the website Forestmosa. The result of data analysis validates a theoretical model that "web visual aesthetics perception" positively affects "positive emotions", "satisfaction" and "aesthetic experience value". "Positive emotions" positively affects "aesthetic experience value" and "satisfaction". "Aesthetic experience value" positively affects "satisfaction", and "satisfaction" positively affects "purchase intention". Also, the results show that regardless of the frequency of online shopping, improving the visual aesthetic perception of web pages can positively affect the aesthetic experience value. Especially, the visual aesthetic design of the shopping website's webpage is obviously important for online shopping low-frequency users.
Cervical Cancer Diagnosis using CervixNet - A Deep Learning Approach

Cervical cancer affects 570,000 women globally and is among the most common causes of cancer-related deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide low-cost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Bio-medical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and Mobile-ODT Kaggle dataset giving an Accuracy of 96.77% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost.
Detecting Distraction of Drivers Based on Residual Neural Network

With the popularity of mobile internet and social software, drivers are getting more and more distracted, leading to a great threat to road traffic safety. This paper put forward the ResNet to identify the distracted behaviors by analyzing the images obtained by a camera module installed above the dashboard in front of the driver. The network with wide residual blocks and improved metric learning method was validated in Tensorflow and showed an ideal accuracy in distraction recognition, providing a new attempt to improve the driving safety.