Miracle-dz/UniFAR-FTUs huggingface dataset:
Study and Comparative Analysis of InAs and GaN-Based High Electron Mobility Transistors

In this article, a comparative analysis of InAs and GaN-Based High Electron Mobility Transistors (HEMT) is considered and simulated to study their characteristics. The device is formed using a 2D Electron Gas (2DEG) at the interface of the InAs/InGaAs and AlGaN/GaN layers. The InAs and GaN-Based HEMT device structure are optimized for high-power operation by employing a larger barrier thickness and a high sheet density of the 2-DEG. To obtain a solution of Poisson’s equation is coupled with the 1-D Schrodinger equation to derive the mathematical framework for InAs and GaN layer to compute the surface potential, gate current, gate voltage, gate capacitance, I-V characteristics, cut-off frequency, and output conductance.
Multimodal Neuroimaging for Early Alzheimer's detection: A Deep Learning Approach

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent research endeavors will prioritize the validation of our model in more extensive, multi-center cohorts and the examination of its efficacy in forecasting the advancement of detection and the effectiveness of therapeutic interventions.
Fake News Detection Using Deep Learning and Transformer-Based Model

Fake news has a tremendous impact especially in certain fields like politics and economy in our society. The rise of social media usage to an extent is in favour of fake/false news promotion. A strong solution in this area would have numerous advantages for the society as a whole. Various deep learning techniques have been suggested by researchers to address this issue. But the majority of them don't identify false news with the requisite precision. In this paper, we propose a fake news identification model with state of art accuracy. Along with that, we compare different deep learning models in combination with different word embedding techniques for the task of fake news detection. Two types of word embeddings, Glove and Word2Vec were used for converting text to a vector of numbers. Along with the word embedding techniques we used seven different combinations of deep learning models: RNN, LSTM, Bidirectional LSTM, GRU, Bi-directional GRU, CNN-LSTM, and CNN-Bi-directional LSTM. The proposed work also implemented FastText and BERT models for embedding and classification of false news. We discovered that the proposed BERT classification model performed most effectively for detecting fake news on the experimented dataset. The proposed BERT model outperformed various baseline models on the benchmark dataset, attaining a test data accuracy of 99.20%, establishing its state-of-the-art performance. For the evaluation of models, we used accuracy, precision, recall, and F1-score metrics on the test data.
Software Defined Network Implements Real Time Detection and Mitigation of DDoS attacks

A Software Defined Network (SDN) controller might keep an eye on the entire network. Unquestionably, distributed denial of service (DDoS) attacks can affect both the controller and switch of an SDN. These kinds of attacks often target the flow tables of the deployed network switches to overwhelm the system, create substantial network delays, and consume bandwidth. Despite the number of conventional detection techniques that are already available, DDoS attacks continue to rise in rate, quantity, and amount of work done. In order to categorize DDoS attacks in an SDN simulation environment, this paper presents an analysis of the issue and makes implementation recommendations for the proposed detection and mitigation algorithm. To do this, the sFlow programme was used to simulate DDoS attacks. Experiments demonstrate that this model is highly effective at detecting security events and at real-time DDoS attack mitigation.
Efficient Real-Time Task-Based Scheduling Algorithms for IoT-Fog-Cloud Architecture

Integrating the Internet of things (IoT), fog computing, and cloud computing (called IoT-fog-cloud) brings a new direction to solve the problems related to real-life applications, such as self-driving cars, health care, autopilot aircraft, traffic management, fire detection and fighting, and many more. Such applications and their associated tasks are time-sensitive and deadline-oriented. However, executing these tasks in the cloud processors, irrespective of the requirements, cause enormous delay and performance degradation. Therefore, these tasks are categorized into hard, firm and soft; and executed on IoT devices or embedded processors, fog processors and cloud processors, respectively, based on their requirements. But, the execution order of these tasks on different processors is challenging and not emphasized in the literature. Recently, a variant of the earliest deadline first (EDF) has been used to determine the execution order. In this paper, we present three different real-time task scheduling algorithms, namely first come, first served (FCFS), EDF shortest job first (EDF-SJF) and FCFS-SJF for IoT-fog-cloud architecture to determine an efficient ordering of the real-time tasks. These algorithms find the assignment test to allot each task to one of the processors. Then each processor uses min-heap to select a task from its queue in order to execute the same. These algorithms are tested using 100 to 1000 tasks (1000 to 10000 tasks) in a uniprocessor (multi-processor) environment and compared using three performance metrics: success ratio, response time and throughput. We found that FCFS-SJF performs better in most cases than other algorithms.
Enhancing Accuracy and Correlation Coefficients of Cancer epitopes through Machine Learning Approaches

Determining the epitope’s binding regions is incredibly difficult. Scientific research on epitopes in folded proteins is still challenging and time-consuming. Various services have been created in the past to forecast B-cell epitopes. Due to the diversity of datasets and the fact that the same technique does not produce acceptable accuracy for all window lengths, the server's accuracy has only made moderate improvement despite tremendous effort. In this study, two models were subjected to machine learning approaches. Using several models, such as IBK and Nave Bayes, both models are trained to distinguish between epitopes and non-epitopes. For Model 2 we achieved an improvement in Mathew's correlation coefficient in the range of 0.9-1.0 and for Model 1 in the range of 0.5-0.6.
Plant Stem Disease Detection Using Machine Learning Approaches*

The rapid identification of plant stem diseases is crucial for implementing timely intervention and minimizing crop loss. While previous research has primarily focused on leaf-based disease detection, this paper proposes an automated stem disease detection and classification model using digital image processing and machine learning techniques. A dataset comprising 3789 images of diseased and healthy stems, categorized into five classes (stem rot, gummy blight, blackleg, didymella, and healthy), was split into training (80%) and testing (20%) sets. Our experiments were conducted on multiple platforms, including Google Colab, Jupyter Notebook, and OpenCV, and compared the performance of four classification techniques: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Impact Learning. Various performance metrics, such as accuracy, precision, recall, and F1 score, were employed to evaluate the classifiers. Our findings reveal that SVM outperformed the other classifiers, achieving an average accuracy of 87%, followed by Random Forest (79%), KNN (75%), and Impact Learning (70%). This research offers valuable insights for farmers and the agricultural industry, paving the way for future studies exploring disease detection in other plant parts using similar techniques.
Exploring the Vulnerabilities of IoT Devices: A Comprehensive Analysis of Mirai and Bashlite Attack Vectors

Our paper provides crucial insights into IoT device security, emphasizing the need for risk mitigation on the edge. This research not only raises security awareness among manufacturers, developers, and end-users, but also guides the development of better defense mechanisms and security protocols for IoT devices, including edge computing environments. The findings can influence policy and regulation, leading to the creation of security guidelines and standards for IoT manufacturers, ensuring robust protection even at the network periphery. This research fosters a safer environment for IoT growth in various sectors, educates users on potential risks and steps to secure their devices, and underscores the importance of edge security. Moreover, the study stimulates further research in the field, advancing the collective understanding of attack vectors and development of novel security solutions that cater to the unique challenges of edge computing.
Optimization of Power allocation coefficient for two-user NOMA using Nature-inspired optimization algorithms

Power domain Non-Orthogonal Multiple Access (PD-NOMA) is an emerging name amongst the candidate technologies which are being considered for the deployment of fifth-generation networks. This medium access technique employs superposition of signals at the transmitter side for transmission and successive interference cancellation to retrieve the separate signals at individual receivers. For maximum utilization of PD-NOMA technique, the optimization of the power allocation coefficient values used for the superposition of the signals is crucial. This paper proposes nature inspired (NI) algorithms viz. Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Differential Evolution (DE), and Simulating Annealing (SA) for finding the optimum value of the power allocation coefficient for two user PD-NOMA. Numerical results show that by calculating power allocation coefficient using NI algorithms, the performance of the PD-NOMA has been enhanced in terms of Probability outage with respect to SINR. Moreover, simulations are performed to calculate power allocation coefficient by varying distance between two receiver nodes. Further, a comparison between different NI algorithms based power allocation coefficient calculations is compiled in terms of computational time and convergence and it is observed that the DE algorithm-based power allocation co-efficient calculations outperforms all other algorithms by 42.65%.
Artist-Specific Analysis of Spotify using BI Tools

Spotify is one of the world’s largest music audio streaming and media service provider with over 489 million monthly active users, including 205 million paying subscribers as of December 2022. The music from various artists around the globe can be found on Spotify where songs from these artists compete daily in order to top the charts. In this paper, we present an analysis of the music charts using many features that describe a song such as acousticness, danceability, etc., and present artist-specific suggestions that can help the artists know the genre of the songs that is most popular among the listeners and therefore, produce songs accordingly and hence help the artists gain popularity among the audience.
Optimization Techniques on Heart Failure Prediction

In this article, we want to draw attention to some of the challenges that come with using conventional optimization strategies, such as the approach that starts with the initial solution and converges to the best one after each iteration. The initial approximation used will determine the convergence. These techniques are inappropriate for objective functions with discontinuities. So, a non-traditional approach was judged to be necessary. And later we will highlight various non-traditional methods, these methods include Genetic algorithms (GA), Cuckoo search algorithm, Ant colony optimization (ACO), Particle Swarm Optimization (PSO), Whale Algorithm, Firefly algorithm, and SA. The later section will explain each of them in great depth and implement all the above algorithms to a suitable data set and measure the factors like Precision, Recall, True Positive Rate, False Positive Rate, and Error Rate into evaluation metrics to conclude our research objective.
Experimental analysis of the symmetry of approximate adder designs in FPGA and ASIC

The approximate hardware design is a suitable candidate for newer custom designs to improve performance, resource usage, and energy efficiency. Many researchers have used different approaches to creating simulated experiments, such as FPGA and ASIC. This fact can imply deviation in the independent variable under observation, i.e., the area utilization, and power dissipation, which can vary asymmetrically. However, how asymmetrical is this variation? What if we used a high-level synthesis approach? Can a characterization of the ASIC design be transferred to an FPGA design? Thus, this research has analyzed power dissipation, and resource utilization (LUT+FF) in approximate adders to characterize the resource usage of design accelerators in FPGA and ASIC from the EvoApprox library. The application to be tested was the Sobel filter for image processing. We used Polarfire, Arty 35T, and Alveo U250 FPGAs. The hypotheses of the experiment were evaluated using three different statistical tests, One-Way ANOVA, Tukey’s test, and Bartlett’s test. We found that even though ASICs and FPGAs are commonly considered with different resource usage, approximated designs in ASIC can be statistically similar to FPGAs depending on the application, and the difference between them could be negligible for power and mostly for the area.
Calibration of a metal oxide sensor for ammonia detection targeting IoT solutions

The use of metal oxide sensors for air quality measurement is advantageous due to the low cost of the sensors, response time, and ease of operation. The MICS-6814 sensor consists of a package of three sensors that can measure various gases. Due to the metal oxide composition of the sensor, its response is affected by various external factors such as ambient temperature and relative humidity. In this work, we investigate the use and calibration of the MICS-6814 sensor in an IoT solution for the detection of ammonia (NH3) in closed environments. The calibration is based on correcting for the effects of ambient temperature and relative humidity using the multiple linear regression method. The calibration process was performed in laboratory, with tests of temperature and relative humidity variation and with ammonia (NH3) in a closed glass chamber. The developed system was able to detect the presence of ammonia in a closed room used to simulate the escape of this gas in an industrial environment.
Security Provisioning as Integrity in wireless sensor networks (WSN): A Survey

Wireless sensor networks (WSNs) are composed of small sensory nodes with limited energy that are deployed in large numbers in order to monitor a physical event in the area to be watched. These nodes have been built to cooperate and operate independently from a centralized infrastructure. The data sent to the sink node from these sensor nodes is seriously threatened by a range of attacks from malicious nodes. In crucial applications like military surveillance, data integrity is a major problem when it comes to security concerns. In order to pave the road for future researchers with a deep understanding of data integrity in WSN, this study analyzes data integrity research from a variety of angles. The limitations of sensor nodes prevent the deployment of traditional data integrity protection measures in WSNs, which require complicated computations. Researchers have recently devised a number of less sophisticated and unique strategies to ensure and enhance data integrity using various cryptographic techniques.
A Systematic Review of Question Classification Techniques Based on Bloom's Taxonomy

Learning knowledge is simply one aspect of education but thinking skills are also taught. Bloom's taxonomy is mostly used for question formulation and evaluation of examination questions. The improvements to Revised Bloom's Taxonomy, give an order of cognitive as well as knowledge-based skills by Anderson and Krathwohl. The fundamental element is the question, which can be used to assess the student's abilities and levels. This paper's main goal is to conduct a comparison of the classifying questions using Bloom's Taxonomy using various methods for improved efficiency and accuracy levels. In this paper, we have explained distinct methods for the classification of exam questions which can be combined to give better results. We also compared various studies using a combination of different techniques for question classification.
An Ensemble Transformer Model For Speech Emotion Recognition

Audio constitutes an indisputably indispensable attribute of nature. The study of sound becomes an interesting endeavour in this regard to expand our knowledge of nature and its pertinent peculiarities. One such characteristic intrinsically associated with audio is the emotion or the sentiment that it relays. Emotions play a deep role in understanding the human psyche and mindset in a behavioural context and advance domains of sociology, psychology, et cetera. It is with this vision of advancement that in this paper, we explore the realm of speech emotion recognition and undertake the endeavour to a comparative study of previously extant popular deep learning algorithms like - CNN & LSTM with certain temperaments of our own, in terms of the architecture and the hyperparameters used, and take into account the performance metrics to propose a transformer model incorporating bidirectional-LSTM, encoder, decoder and scaled-dot product attention. A popular standard - the RAVDESS dataset has been used for this purpose. The model has shown promising results on a preliminary basis when subjected to different metrics of testing and validation, and can potentially be employed in high-precision requisite systems.
Biofusion: Expanding Biometric Horizons with ECG and PCG Integration

In recent years, biometrics has emerged as a highly reliable and authentic method for person identification. We proposed a novel method in this paper for biometric identification based on the fusion of electrocardiogram (ECG) signals and heart sounds in the form of phonocardiogram (PCG) signals. A self-collected dataset from 32 individuals by using BIOPAC MP-36 systems was utilized. Raw signals were preprocessed using 3 types of IIR filters. Subsequently, cepstral features were extracted and fused serially, resulting in combined information from both ECG and PCG signals. Different classifiers were tested for performance comparison. Linear discriminant classifier as well as ensemble subspace discriminant classifiers outperformed the others depicting an accuracy of 100%. The results demonstrate that highly accurate biometric identification can be accomplished by the fusion of ECG and PCG signals. Moreover, the proposed system demonstrated promising accuracy levels for a wide range of signal-to-noise ratios under various noisy conditions.
Implementation of Efficient Restoring and Long Division Algorithm

The basic elements of an electronic system are arithmetic operations. Arithmetic operations are the building block of any electronic application and algorithm is a sequence of instructions used to carry out calculations or solve problems. Division implementation has received less attention than the other arithmetic operations, despite the fact that addition, subtraction, multiplication, and division are essential components of arithmetic implementation in the electronic system. If we divide two integers using the procedure, a quotient as well as a remainder is produced. Implementing division operations is quite difficult; in this case, a sophisticated technique is used for implementation success. A successful system must have good division circuit performance. In this research work, the Restoring division and long division algorithm Class are implemented for nsigned integers with data sizes of 8-bit, 16-bit, 32 bit and 64-bit using Verilog HDL language. Three registers named A, Q and M are used for the computation in both the algorithms.
A Review of Data Security in Cloud Computing

Cloud computing has evolved as an effective model for delivering computing resources and services over the Internet. While it has many advantages, such as scalability and cost-effectiveness, the security of data stored and processed in the cloud is still a key worry. This article provides an in-depth examination of data security in cloud computing, emphasising the different difficulties, solutions, and emerging trends in this subject. We investigate the most serious threats to data security, such as unauthorised access, data breaches, and insider attacks, as well as the tactics and mechanisms used to minimise these risks. Furthermore, we cover developments in encryption, access control, authentication, and other security methods that improve the security of sensitive data in the cloud. This review's goal is to evaluate and summarise available studies.