Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Systems Success Models in the E-Government : Context: A Systematic Literature Review

In their work to date, researchers around the world have faced the serious challenge of measuring and evaluating the success of information systems. The increasing development and use of e-government systems in the last decade leads to the need to research models of IS success in the context of e-government. This paper uses systematic research and literature review to identify several different approaches to measuring IS success in the context of e-government. The results show that in most cases the success of the e-government system was examined using the Davis success model DeLone and McLean IS, the Technology Acceptance Model (TAM), as well as the proposed new hybrid models, which are a combination of several models. The results of this study will add existing knowledge from past studies that have integrated IS success in the context of e-government.
Extraction of DMTL Equivalent Circuit Parameters Using SMPSO

This paper proposes the application of soft computing techniques to extract the equivalent circuit parameters of distributed MEMS transmission line (DMTL). In this work, we used speed-constrained multi-objective particle swarm optimization (SMPSO) algorithm to extract the values of the LC network which is a part of DMTL equivalent circuit. We compared the extracted results with the reported values. The results agreed with the reported values.
Mtanh-Attention-BiLSTM model for prediction of Automobile Export Customer Satisfaction.

Attention based Bidirectional LSTM is used in machine learning for Data Classification and predictions. The activation function is one of the important aspects which facilitate the deep training by introducing the non-linearity data into the learning & linear process. There are various types of existing activation functions like Sigmoid, tanh, Relu etc. To increase the performance in terms of accuracy the novel research work Mtanh-Attention-BiLSTM model for classification and prediction was proposed. This novel activation functions reduces the gradient problems and transform the nonlinear into linear form. The training and classification was done with the primary export dataset and standard car dataset also. The performance improvement was observed with increase in accuracy compared with the existing model.
A Novel Bimodal Fusion-based Model for Depression Recognition

Depression is a common mental disorder which is harmful to our family, economics and society. Many people cannot receive timely mental health services, and the diagnosis process is subjective. A primary way for reducing harm is finding an objective and effective depression detection approach. Speech and video are two promising behavior indicators for depression. In this paper, we proposed a speech and video bimodal fusion model based on time-frequency analysis and convolutional neural network for this goal. For the testing of the proposed method, a speech and video dataset of 292 participants were employed for cross-validation. Compared with the single modal classification results, the classification accuracy and generalization ability of this gender-independent model are further improved, which is helpful for the identification of depression.
Interleaved Modulation Scheme With Optimized Phase Shifting for Double-Switch Buck-Boost Converter

Owing to low voltage stresses, wide range of input voltage and the same polarity of output voltage and input voltage, double-switch buck-boost converter (DSBBC) is a popular choice to achieve dc/dc conversion in photovoltaic generations (PV), power factor correction (PFC), portable devices and so on. Combined control (CC) makes DSBBC operate efficiently, but large ripples of output voltage are unavoidable during mode transition. With two equal duties, interleaved modulation (IM) makes the inductor ripple current decrease remarkably compared with synchronous modulation (SM) while inductor average current is still high. By introducing duty offset, inductor average current declines and efficiency is enhanced obviously under interleaved modulation with duty cycle offset (IMDO). However, with 180-degree phase-shifted angle, the ripple current is not low enough in some cases. Thus, to make further improvement on ripple current, an interleaved modulation scheme with optimized phase shifting (IMOPS) is proposed for DSBBC in this paper. Current features, optimal phase-shifted angle (OPSA) and operating region under IMOPS are described meticulously. Besides, theoretical and quantitative comparisons of different modulation schemes are presented, including inductor average current, inductor ripple current and ratio of direct power transfer (DPT). Based on a 150W prototype, the feasibility and effectiveness of IMOPS are demonstrated by experimental results.
A Review: Evolution of Technology towards Green IT

Today, technology is taking its place in every single field; whether it is to ease the life of humans or to save the human life, technology is all around. Due to this tremendous growth, the users of computers and other related resources (like data centers, hardware resources like sensors, printers, storage devices etc.) have increased exponentially. This has resulted in huge manufacturing, usage and wastage which is eventually contributing to low energy-efficiency, more power consumption, excess carbon footprints thus leading to global warming, pollution, and other environmental issues. Keeping in mind the environmental concerns, it has become a need to impose some strict measures to minimize the cause. Optimal utilization of these resources is the only root step towards IT sustainability. This eventually led to the concept of green computing. This paper shows the journey of eco-friendly technology from its origin to its future.
2D EEG Topography and Spectral Analysis for Monitoring Working Memory Status of Smokers

In the abstinence of nicotine, smokers have difficulty to stay focus, handle stress and keep productive. Monitoring working memory performance of smokers is essential during the therapy to cure addiction. The monitoring has not been studied using non-invasive EEG. This paper describes the 2D topography and spectral analysis of smokers' EEG signals to identify the working memory status. The purpose of the study was to select the suitable tasks to be utilized in the neurofeedback system for curing smoking addiction. The EEG signals were recorded from smokers during memory tasks before and after smoking. The delta and alpha band of the EEG signals were filtered and the average power spectral density was computed. The 2D EEG topography was then carried out to analyse the working memory state of each subject. It was found that the working memory of the smokers was more active after smoking and the tasks employed in the study could reveal the brain state of smokers.
Morphine-induced Cognitive Dysfunction Model in Zebrafish

The neural pathway of opioid use plays a crucial role in drug abuse, cognitive alteration, and abnormal change in neurobehavior. This study aims to investigate the effect of morphine on changes in cognitive aspect through assessment of the startle habituation response in the zebrafish model. Zebrafish embryos were exposed to 0.8 µM morphine for 5 days, starting from 5-hour post-fertilisation (hpf). On 6-day post-fertilisation (dpf), the larvae were transferred to the custom startle apparatus for assessment. The recorded videos were analysed using Toxtrac software. From the analysis, morphine-treated larvae showed impairment in the habituation response, with excessive startle response and non-consistent decrement in habituation. Besides, chaotic movements and preference towards peripheral zone in the larvae treated with morphine also signifying anxiety. We postulate that the morphine regulated pathway are involved in the cognitive alteration and abnormal behaviour of morphine-treated zebrafish. Future studies are required to associate the morphine-induced behaviour to the neural circuitry and firing activities.
Module Placement Scheme Using MPC4.5 with Markov Chain Process for Mobile Fog Computing Environment

Mobile Fog computing (MFC) is an elongation to cloud computing in which the processing is performed near the IoT devices. It reduces latency and energy consumption in the system and the idle choice for healthcare applications, smart cities, etc. In MFC, the mobile devices (MDs) select the best fog device (FD) and offload FDs' task. The submitted applications to the system are divided into modules, and the best FD has been selected for the module using the Module Placement C4.5 (MPC45) algorithm. If the MD's power consumption is more than that the power consumption of Wi-Fi, then only offloading has been performed. There are many factors, such as authenticity, confidentiality, availability, capacity, integrity, cost, and speed are used as the decision parameters for selecting the best FD. In the present research paper, a novel module placement scheme has proposed for optimizing the MPC45 with the Markov chain. The proposed approach is compared with the state of art algorithms in terms of power consumption, performance, and response time, which shows that the proposed method of task offloading is better compared to the other existing ones.
Stabilization Domains for Second Order Delay Systems

In this paper, an analytic condition is given for determining delayed positive feedback controller for stabilizing an oscillatory system. The <inline-formula> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula>-decomposition and <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>-decomposition methods are employed in deriving this condition. The obtained results are then used to stabilize second order delay systems by a proportional controller. Under-damped and over-damped systems are treated separately, where the Smith-predictor structure is used in the over-damped case to obtain the stability conditions. Illustrative examples are given to show the effectiveness of the proposed approach.
Channel Estimation Based on DFT Interpolation and Out-Of-Band Spatial Information in Multi-User Millimeter Wave Systems

Millimeter wave (mmWave) MIMO communication can greatly increase the data rate, but the huge overhead of channel estimation cannot be ignored. In order to reduce training overhead, some researches based on the spatial channel correlation between the sub-6 GHz band and the mmWave band have been carried out. However, their performance is not satisfactory in distinguishing two users with close angular distance. In this paper, we propose a novel channel estimation method based on out-of-band information to enhance the accuracy of channel estimation in multiuser scenario. More specifically, based on the observation that dominant paths in mmWave band are a subset of that in sub-6 GHz band, we first obtain a coarse estimate of the spatial angle information from the sub-6 GHz band, and then, we use the discrete Fourier transform (DFT) interpolation method to refine the the estimation in mmWave band with low the training overhead. Numerical results show that, compared with the existing out-of-band information based channel estimation methods, the proposed method has better performance in multiuser scenario and the training overhead can also be reduced.
Vertical Localization by Multipath Analysis in Artificial Human Ears for Robots

Sound Localization in robots especially in the vertical plane are either by using microphones array or using HRTF. While HRTF uses the characteristic of human ears that distort a sound frequency. We propose the multipath analysis to localize the sound source in a vertical plane. The multipath is the result of sound reflected in the pinna of the human ear before coming into the inner ear. The analysis of the sound multiple frequencies shown that the polynomial model of 1500 Hz has the best result in vertical plane sound localization. The angular error of this model is 8.407 and RMSE is 10.308. Similar results can also be seen from other frequency models that were built in this study. Those results showed that we can localize the sound in the vertical plane using multipath analysis that was recorded using only 1 microphone equipped with the artificial ear.
Statistical analysis to determine the ground truth of fatigue driving state using ECG Recording and subjective reporting

In 2018, the World Health Organization reported about 1.35 million deaths caused by traffic accidents, which are also the leading cause of death of children and adolescents globally. Therefore, an early-warning system is needed to prevent accidents caused by fatigue driving. Research on driving-fatigue detection has determined the ground truth of the fatigue state by designing tests for specific conditions, designing tests at particular times, and relying on subjective reporting. Because determining the ground truth of the fatigue state affects the results of fatigue detection, this paper advances this investigation of the ground truth of the fatigue driving state and driving times using electrocardiogram recordings and a questionnaire that subjectively captures drivers' fatigue states. After five test sessions and heart-rate statistical data analysis, the minimum time required to induce a fatigue driving state in a driving simulation was 90 minutes with a Chalder Fatigue Scale score of 16. The driving-simulation software also affected the drivers' heart rates, however, so better programmable driving-simulation software is needed to create specific conditions, such as traffic density and limited driving speed, to induce realistic fatigue driving states.
A Review on E-waste: Fostering the Need for Green Electronics

With rapid development in technology, electronic waste (e-waste) or Waste Electronic and Electrical Equipment (WEEE) is an arising threat, posing serious contamination problems to mankind and the environment. The fundamental reason behind uncontrolled electronic waste around the world is the fast advancement of innovation and low production cost. Due to this, very large amounts of e-waste have to be discarded every year whose disposal is a major concern. To tackle this problem of e-waste, e-waste management methods like reducing and recycling play a vital role. These techniques also help in the establishment of a circular economy. This paper summarizes the statistics of e-waste generated worldwide, along with focusing on the benefits of recycling. It highlights the impact e-waste has on the environment and mankind and how green electronics could be one of the viable remedies to this.
Estimation of Health and Initial SOC based on Voltage Variation for LiPo battery

This paper aims to evaluate and exhibit the State of Health and initial State of Charge of a Lithium-ion Polymer Battery, which offers high power and energy density. SOH and SOC estimations are vital in terms of safety and fidelity of electric vehicles. As the other estimation techniques are quite challenging with complicated algorithms and models, the proposed method just uses voltage as a common factor, because of its dropping nature, along with the battery’s age. The two salient features of Battery Management System (BMS) are SOH, determined with the voltage variation in the charging phase and SOC, determined using the exact voltage present in the battery at that instant and the maximum voltage obtained during each full charge cycle under no load conditions. The experimental setup includes a charger, LiPo battery of 11.1V, Arduino (ATMEGA 328 for the UNO) on I2C protocol, and a relay. The battery’s full charge voltage is stored in Electrically Erasable Programmable Read-Only Memory (EEPROM) of the Arduino, which acts as an input for both the estimations. This study concentrates on the full charge voltage, each and every time, regardless of the age and condition of the battery for improvisation in the accuracy.
Layer-Based Attacks in the Ternary Planes of Software-Defined Networking

Software-Defined Networking is an emerging paradigm that anticipates changing the constraints followed by conventional networking architecture. It separated the control plane from the data plane and make the control plane a centralized hub of the network. By centralizing the controller, SDN carries a good hold over the whole network as the controller is having the information of every node of the network and provides an ease to the users by offering programmatic access. But on the other side of the coin, the criterion of security has to be done manually as it increases the working load of the networking administrators. So, the paper provided a comprehensive survey of different attacks in each layer of the SDN environment. In this paper, each plane and the interface of SDN are categorized further into different attacks that can occur in a particular stance. To the best of our knowledge, the proposed research work is the first-ever attempt to detect the attack in each layer as well as in between the interfaces of every layer of SDN. This work can be very essential for the network administrators and the researchers working in the field of attacks in SDN to find the layer-based attacks.
Audio Signal Mapping into Spectrogram-Based Images for Deep Learning Applications

Various features generated from raw audio signals can be used as an input of a deep learning model. They include hand-crafted features such as mel-frequency cepstral coefficients, two-dimensional time-frequency representations and raw audio data. In most cases, the time-frequency representations are related to so-called spectrogram-based images. Having an image at the deep learning input enables to apply performance improvement accumulated in video and image processing. However, spectrogram-based images have some specific properties that should be taken into account when a deep learning model is designed. This paper deals with mapping of audio signals into the most common spectrogram-based images. Some unique properties of these images as well as the way how they are generated are analyzed here for a particular case of fridge sounds.
WSN based Advanced Emergency Fire Alarm System Using MQTT Centric Star Topology

This project proposes an ‘Advanced Fire alarm’ system based on ‘Wireless Sensor Network (WSN)’ in order to ensure safety and security, especially for industries which in turn save lives and property. In short, it works automatically based on the instantaneous atmospheric variation related to temperature and gas leakage, and confirms the danger yet to occur. WSN has been interfaced with the whole system. Traditional wiring systems excluded hereby introducing ‘Internet of Things (IoT)’. The system divided into two parts such as ‘Master Control Panel’ and ‘Child Sensor Nodes’, whereas a master can control the information provided by multiple peripheral devices and log it to the website. Each device consists of two sensors such as MQ-2 sensor, $100\ K\Omega$ NTC thermistor, and ESP8266 Wi-Fi module. The information of two different parameters associated with fire has been monitored through child devices. Master device collects all data and updates the server. As a result, user can monitor them through IoT panel. The web panel is equipped with ‘Message Queuing Telemetry Transport (MQTT)’ broker, gather information about gas leakage and temperatures in the environment. The time response of sensor nodes, amount of time dissipates to receive data in control panel, and manual control has been recorded. All the child devices can talk to the master control panel and exchange data between them as a form of Star Topology architecture. These factors will make the system much easier to operate and seed information from device to device.
A Survey on Visual Speech Recognition Approaches

The robustness of automatic speech recognition (ASR) systems degrade due to the factors such as environmental noises, speaker variability, and channel distortion, among others. The approaches such as speech signal processing, model adaptation, hybrid techniques and integration of multiple sources are used for ASR system development. This paper focuses on building a robust ASR system by combining the complementary evidence present is the multiple modalities through which speech is expressed. Speech sounds are produced with lip radiation accompanied lip movements called Visual Speech Recognition (VSR). VSR system converts lip movement into spoken words. This system consists of lip region detection, visual speech feature extraction method and modeling techniques. Robust feature extraction from visual lip movement is a challenging task in VSR system. Hence, this paper reviews the feature extraction methods and existing databases used for VSR system. The fusion of visual lip movements with ASR system at different levels is also presented.
Monitoring Neuronal Dynamics in the Ventral Tegmental Area Using an Implantable Microimaging Device With Microdialysis System

To monitor dopamine neural activities in the ventral tegmental area (VTA) in response to nicotine, we designed a dopamine platform based on a microimaging device and a microdialysis system. We measured dopamine (DA) release in the prefrontal cortex (PFC) and the nucleus accumbens (NAc) by a micro dialysis system and monitored DA neurons activities by fluorescence imaging in the VTA using a microimaging device simultaneously. GCaMP6 transgenic mice were used in this study, and the change in the fluorescence ratio intensity associated with nicotine administration was estimated. Our results suggested that nicotine administration increased the DA neurons activity in the VTA as well as the DA release in the PFC and the NAc. The proposed platform has the potential to monitor neural activities in the deep brain regions of rodents.
Automatic Diagnostic of the Presence of Exudates in Retinal Images Using Deep Learning

Diabetes is one of the fastest-growing chronic diseases in the world. Diabetic retinopathy, a complication of Diabetes that affects vision, and if not treated promptly, can lead to total blindness of the patient. This abnormality has no cure, but if discovered in its early stages, there is a high chance that the patient will not reach total blindness. Detection of retinal background exudates is essential for the early diagnosis of diabetic retinopathy. In this paper, we present a deep learning model with a Convolutional Neural Network to diagnose exudates' presence or absence. The best results are about 99.52% sensitivity, 100% specificity, and about 99.76% accuracy for 1,608 images. Thus, the authors believe the proposed method can integrate a clinical system.
A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment

In Vitro Fertilization (IVF) is the most widely used Assisted Reproductive Technology (ART). IVF usually involves controlled ovarian stimulation, oocyte retrieval, fertilization in the laboratory with subsequent embryo transfer. The first two steps correspond with females' follicular phase and ovulation in their menstrual cycle. Therefore, we refer to it as the treatment cycle in our paper. The treatment cycle is crucial because the stimulation medications in IVF treatment are applied directly on patients. In order to optimize the stimulation effects and lower the side effects of the stimulation medications, prompt treatment adjustments are in need. In addition, the quality and quantity of the retrieved oocytes have a significant effect on the outcome of the following procedures. To improve the IVF success rate, we propose a knowledge-based decision support system that can provide medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle. Our system is efficient in data processing and light-weighted which can be easily embedded into electronic medical record systems. Moreover, an oocyte retrieval oriented evaluation demonstrates that our system performs well in terms of accuracy of advice for the protocols and medications.