Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense-UNet with Network-Based Transfer Learning for Left Ventricular Wall Segmentation

The Left Ventricular Wall (LVW) is frequently becoming a region of interest in ultrasound examinations. Its abnormalities often serve as early indicators of various cardiovascular diseases. However, the poor-quality ultrasound images make it essential to develop automated segmentation methods so doctors can observe LVW's structure and movement more clearly. This study proposes a deep learning approach to solve this challenge. The main improvement is the construction of Dense-UNet architecture coupled with network-based transfer learning, called Dense-UNet-121, Dense-UNet-169, and Dense-UNet-201 with transfer learning. They are hybrid deep learning architectures in which the structure and parameter values are partially transferred from models trained on different tasks and datasets. To validate their performances, they will be compared with the previous state-of-the-art model, i.e., UNet and Dense-UNet without transfer learning using publicly available datasets from Hamad Medical Corporation, Qatar University, and Tampere University (HMC-QU). The results demonstrate the consistent superiority over all previous state-of-the-art model. Additionally, the usage of transfer learning also provides higher accuracy, smoother learning curves, and a training duration that is 34.7-36.3% shorter. Dense-UNet-201 with transfer learning attains the highest accuracy values of 0.9951, 0.9708, and 0.9698, respectively for training, validation, and testing data, confirming the excellent result of this approach.
A Novel Malware Detection and Classification for Healthcare Apps

With the widespread adoption of digital technologies and the importance of protecting sensitive data and essential infrastructure, the cybersecurity environment is facing more challenges. This paper shows a novel, advanced approach to detecting and categorizing Android malware that extends beyond what has previously been done in the domain of Android security, with a particular emphasis on applications related to healthcare. Through the use of several data sources to collect health-related applications and cutting-edge methods like transfer learning and machine learning classifiers, a thorough framework is constructed. It uses BERT models for extracting out static features, optimizes feature selection, and uses machine learning algorithms to get a 99.47% success rate. By improving detection and classification capabilities, this novel approach shows promise in protecting healthcare apps, providing strong protection in the ever-changing field of Android malware.
Fine-Grained Gesture Recognition by Using FMCW Millimeter-Wave Radar

Accurate recognition of fine-grained gestures is a prerequisite for their application in emerging scenarios, such as smart cars and smart phones. In this paper, we propose a novel neural network based strategy to identify the range, doppler, and angle features inherent in gestures acquired by millimeter-wave frequency-modulated continuous wave (FMCW) radar. First, a dataset with eight different fine-grained gestures is created, where the gesture signals are echoes after dechirping. Since the raw data is difficult to process directly, range, angle and doppler features of fine-grained gestures are extracted with high resolution by using Multiple Signal Classification (MUSIC) algorithm, Short-Time Fourier Transform (STFT), respectively. Particularly, we design an improved Deep Residual Shrinkage Network (DRSN) with variable channels to recognize features of fine-grained gestures. Experiments validate the effectiveness of the proposed architecture, and an impressive accuracy of 98.8% is achieved in the triple-channel network structure.
Designing a New Control Method for Three-Level Active Power Filter

In contrast to the two-level Active Power Filter, the three-level neutral point clamped (ANPC) offers a higher number of output levels, reduced harmonic impact, and diminished equipment losses during harmonic compensation. Nonetheless, the inherent conservatism of single-step prediction limits its reliability to the k+1 time step, failing to provide relevant information for optimizing future steps. Additionally, three-level APF control necessitates concurrent consideration of multiple objectives, resulting in some tracking errors. This study introduces a multistep prediction approach to address these issues. Cycling results in identifying optimal and suboptimal switching states at k+1. Substituting these optimal states at k+2 yields the optimal states across two consecutive control cycles. This method enhances control process reliability, with simulation results demonstrating minimal power grid harmonic content, aligning with the requisite criteria.
A Novel Shortcut between Local Windows for Window-based Vision Transformers

Window-based self-attention (WSA) has been proved to be an effective way to reduce a transformer block’s complexity in window-based transformers. However, the relations between local windows cannot be learned in the WSA module. Thus, some recent transformers create cross-window connections by proposing new attention modules or adding convolutions in the transformer block, while still adopt the WSA module with limited reception field. To provide global information for all transformer blocks in window-based transformers, we propose a local-window shortcut (LWS) which is in parallel with the residual shortcut. LWS contains both spatial and channel transformations. The channel transformations are two linear layers, which not only minimize the parameters and computational cost for LWS but also increase the feature diversity. The global spatial transformation is achieved through an ACDC layer, which is much lighter than a fully connected layer. Extensive experiments conducted on two public datasets (CIFAR100 and Tiny-ImageNet) and a customized mini-ImageNet dataset demonstrate that LWS with suitable window size can greatly boost the performance of some SOTA window-based transformers (up to 1.6% accuracy improvement on CIFAR100), while the parameters and FLOPs increase is negligible.
An Optimization Method for SLAM Feature Point Extraction in Fuzzy Scenes

In order to solve the problem that image blur is easily caused by camera shaking of micro-robots during surgical navigation, which leads to the accumulation of SLAM errors, this paper studies the feature point extraction optimization method based on deep learning in fuzzy scenes, proposes an improved defuzzification network, and finally applies the network to the front end of SLAM, and conducts experimental verification on the open data set TUM. The results show that the proposed algorithm significantly improves the accuracy of front-end feature extraction and matching.
Advancements in IoT-Powered Smart Environment Monitoring Systems

IoT, being an umbrella term for various technologies, has sparked a plethora of research and development in the field of climate monitoring. The ease with which technology nowadays can penetrate deep into forbidden and remote areas that are only possible to reach by us. We see how rapidly the environment is changing and degrading we can observe the rapid change in the pollution rate, excessive water and air pollution, and depletion of greenhouse gases, and in increasing natural disasters because of which its monitoring and protection become a necessity. In order to halt the speedy loss, IoT has been deployed on an enormous scale. With a common goal to achieve sustainability, the usage of IoT is on major growth. This paper focuses on the quality of the environment by checking natural calamities, greenhouse depletion rates, and air contamination. Through the help of IoT, we can now manage these issues through different tools, sensors, and wireless advancement in technology in order to accomplish a smart environment monitoring system (SEM). This paper encapsulates the propagation of IoT technologies which has contributed to making society a healthier and smarter place to live in.
Can Lexicon-Based Sentiment Analysis Boost Performances of Transformer-Based Models?

An essential endeavor in natural language processing, sentiment analysis entails determining the sentiment expressed in a text. Transformer-based models like BERT have attained state-of-the-art performance in sentiment analysis tasks. However, these algorithms may have difficulty distinguishing sentiment-laden words. In response, we proposed combining lexicon-based sentiment analysis and transformer-based models. This study investigates the effect of lexicon-based sentiment analysis, particularly SentiStrength, on BERT's efficacy in sentiment analysis tasks. Experimental evaluations reveal that incorporating sentiment lexicons enhances the accuracy and F1-score of classical sentiment analysis compared to the baseline BERT model. Our findings demonstrate the value of incorporating external knowledge sources into transformer-based sentiment analysis models.
Dynamic Batch Scheduler for Efficient Neural Networks Training

The widespread utilization of neural networks (NNs) in recent years has been driven by their remarkable ability to discern complex data patterns. However, training NN models on vast datasets can be computationally expensive. To address this challenge, we investigate the art of data batch scheduling, which involves strategically organizing extensive datasets into smaller, manageable batches for training. This paper presents a novel dynamic batch scheduling algorithm explicitly designed for training deep neural networks. It dynamically constructs batches based on a combination of training loss and batch utilization, leading to optimized training efficiency. The experiments conducted on MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate the superiority of our approach over recognized methods. Our dynamic batch scheduler, when combined with different, models, optimizers, batch sizes, numbers of batches per epoch, and time complexity, consistently improves training and testing accuracy while maintaining algorithmic efficiency.
Selection of Machine Learning Methods for Keylogger Detection Based on Network Activity

A keylogger is a technology that secretly tracks user input into a system. Attackers use it to steal personal information and credentials, such as logins and passwords on banking websites and electronic payment systems. Therefore, detecting keyloggers is one of the priority tasks for ensuring information security. This paper presents an approach to detecting keyloggers based on a set of artificial intelligence methods that make it possible to identify implicit patterns. We are also experimentally evaluating machine learning models using the same software, hardware and the same subsets of the Keylogger dataset for training and testing. We compare ten intelligent models, including gradient boosting, decision trees, and k-nearest neighbors. This allows us to determine the most suitable keylogger detection models in a single environment.
Simulation of E-Mode p-Channel GaN/AlGaN HFETs with Hybrid AlGaN Barrier Layer

In this paper, a p-channel enhancement-mode p-InGaN/GaN/AlGaN heterojunction field-effect transistor (HFET) with a hybrid AlGaN barrier layer (HBL) is proposed. It is found that the introduction of the HBL and p-InGaN/GaN/AlGaN heterojunction can effectively deplete the two-dimensional hole gas (2DHG) in the gate region and reduce the on-resistance (RON). The TCAD simulation results show that the lower Al molar fraction and the thicker thickness of embedded barrier obtain the more negative threshold voltage $(\mathbf{V}_{\mathbf{TH}})$, without sacrificing the on-state current $(\vert \mathbf{I}_{\mathbf{ON}}\vert)$. In addition, by using p-InGaN/GaN/AlGaN heterojunction to increase the hole concentration in GaN channel, the $\vert \mathbf{I}_{\mathbf{ON}}\vert$ of the HBL HFETs has been increased from 4.3 mA/mm to 9 mA/mm at $\mathbf{V}_{\mathbf{DS}}=-\mathbf{5}\ \mathbf{V}$, with a high $\vert \mathbf{V}_{\mathbf{TH}}\vert$ of |–2| V.
Boundary-based X-Ray Lung Segmentation: A Novel Approach for Improving Accuracy and Efficiency

Lung image segmentation plays a crucial role in medical diagnostics and treatment planning. This research paper addresses the challenge of accurately segmenting lung images from X-rays using a novel approach that combines semantic boundary-based segmentation and the alpha-shape algorithm. Additionally, the global mask concept is introduced to impose constraints on the shape of the output. The proposed method is evaluated against a semantic segmentation approach with full-mask utilization. Experimental results demonstrate that our approach outperforms the baseline method, yielding significant improvements in segmentation quality. The proposed technique achieves a 4% improvement in the Jaccard metric and a 2% improvement in the Dice metric. These findings highlight the effectiveness of our approach, particularly when the objects to be segmented exhibits similar shape patterns.
Realtime Atrial Fibrillation Detection System For IoMT Using Hybrid Machine Learning Classification

Atrial fibrillation (AF) is a type of cardiac arrhythmia in which the atria contract rapidly and irregularly, resulting in an irregular heartbeat. AF can lead to decreased blood flow to the body, blood clots, stroke, heart failure, and other cardiovascular complications. This paper proposes a detection system that would provide real-time AF detection by capitalizing on the power of Deep Convolutional Neural Networks and XGBoost classification models for the IoMT (Internet of Medical Things), which refers to medical devices and applications with Internet connectivity. This paper aims to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services through the proposed detection system.
A Study on Machine Learning Based Classification of Crystal Structures in ABO3 Perovskites

This study focuses on the classification of ABO3 materials using the XGBoost model. Out of 5329 perovskite compounds, 547 compounds are selected after the preprocessing phase of data mining. Some features are dropped due to a lack of relevance to the classification task. The correlated features are identified and redundant features are removed from the dataset. The proposed model successfully classified the structures into their appropriate classes with 90% accuracy. The augmentation technique is applied to deal with the imbalance in the dataset. The model is inexpensive and fast. The crystal structure classification finds application in material engineering.
Hand Gesture Recognition with Deep Convolutional Neural Networks: A Comparative Study

Hand gesture recognition is a growing field with applications in human-computer interaction, sign language interpretation, and virtual/augmented reality. The use of convolutional neural networks (CNNs) has become prevalent in this field as they possess the capability to autonomously extract relevant features from image data, facilitating precise and effective hand gesture recognition. This paper presents a comparison of popular pretrained CNN models for hand gesture recognition, evaluating their performance on three widely used datasets: the American Sign Language (ASL) dataset, ASL with Digits dataset, and NUS Hand Posture dataset. The models were fine-tuned and tested, and the analysis included accuracy, training epoch, and training time. The pretrained CNN models compared include VGG16, ResNet50, InceptionV3, DenseNet201, MobileNetV2, Inception ResNetV2, Xception, and ResNet50V2. The findings of this research can provide valuable insights into choosing an appropriate pretrained CNN model for applications involving hand gesture recognition.
Local Closeness Gravity Model to Identify the Vital Nodes in Complex Networks

In complex network analysis, identifying the viral nodes is a major concern of the research domain by which any kind of information or infection is controlled throughout the entire network. Several algorithms have been developed over the past few years to identify the viral nodes (influential spreaders) considering many properties of the network. Among them, some authors proposed gravity-based centrality to identify the vital nodes based on the law of gravity with certain limitations. The major limitation of existing gravity-based methods is the mass of the object (i.e. node) is considered as the degree or kshell index only, which does not always signify the spreading ability of the nodes. To address this research challenge, we propose an innovative Local Closeness Gravity method (named LCG) to measure the influential ability of individual nodes, facilitating the identification of the vital nodes in the network. To minimize the computational complexity of Closeness centrality, at first, we measure the local Closeness centrality of individual nodes considering all the nodes residing in the truncation radius. Thereafter we introduce a new parameter “information sharing ability” based on connectivity strength to measure the distance between the nodes. Finally, the influential ability of each node is measured based on the gravity model considering the local closeness centrality, kshell index, and the distance. The efficiency of LCG is compared with the existing baseline centrality methods by using the Susceptible-Infectious-Recovered (SIR) simulator. The correlation between the LCG method and the baseline centrality methods with the SIR method is compared by Kendalls' tau method considering various infection probabilities and various percentages of seed nodes respectively. The ranking uniqueness of the LCG method and the baseline centrality methods are also measured by the monotonicity metrics. Through the obtained results and various analyses, it becomes evident that the LCG method adeptly discerns the vital nodes within the networks.
An IoT Based Mood Evaluation Device for Pets: Power of Computer Learning

In this highly advancing era, where everyone is dependent for everything on machines, a tool must be there that can help the human beings to take care of beloved pets. To do so, it is necessary to know about how they feel at some specific time period and its cause. The design that is proposed here contains mainly two aspects, i.e., one using a sensor-based arrangement and the other will be the observed abnormalities through gathered knowledge from their behaviour. Basically, a sound sensing design will sense the bark of the canines and the sensed voice will be monitored and judged with the ideal sounds of the barks already saved in the system in large amount. The system will match the bark through the cross-correlation function applied and give results accordingly. The other aspect acquires the deep study into pets like what they would react according to certain situation. All these factors together will help create a scenario for owner to understand and decide whether their pet is feeling fine or not and act accordingly.
Use of Blockchain Technology in Electronic Voting Systems: An Overview from Computer Security

Block chain is a new and growing technology in the IT world right now. This technology is a way to store data that is not based in one place. It gives centralized data storage systems an alternative that is safer from a security point of view. This paper gives an idea of how Block-chain Technology can be used to protect an e-voting system. In this paper, we looked at all the work that has been done in the same area before. We chose a number of research papers in the same field and looked at them. From what we can tell from these papers, using Block-chain Technology for voting will be very useful. And this system for voting online will be much safer than the one we have now. Electronic voting, or E-voting, is simply the use of computers to cast and count votes. This method makes the voting process faster and cheaper. When more technology is used in a certain area, security problems also get worse. When you vote online, security is important. By using the Block chain and its distributed database system, we can make it harder for people with bad intentions to change the database. This makes the e- voting system more secure.
Detecting Fine-Grained Emotions from COVID-19 Tweets Using Transformer-Based Architecture

With the proliferation of social media, the task of identifying emotions in textual content has become increasingly relevant. Automating the task of detecting emotions from social media content has a wide range of applications today. In this work, we investigate the efficacy of pre-trained transformer models to detect fine-grained emotions from real-world tweets. We conduct experiments to fine-tune the DistilBERT and XLNet transformers to recognize nine emotions including neutral, anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Our DistilBERT and XLNet based models exhibit an F1-score of 0.6429 and 0.6679 respectively. The results are promising when compared with similar transformer-based models for fine-grained emotion recognition. We conclude from our study that fine-grained emotion detection from microblogs using transformers although promising is also challenging. The challenge is primarily on account of two reasons: a) difficulty in differentiating between intrinsically similar emotions and b) difficulty in building a large and balanced annotated corpus from real-world tweets.
Machine Learning-Based Time-Series Analysis of Environmental Parameters for Predicting Occupancy Patterns

We present a learning-based system architecture that uses time-series analysis of indoor environmental parameters to estimate the current occupancy of a room and predict its future occupancy. We have co-developed a sensing monitor that can measure various environmental parameters. A number of these sensing monitors were deployed in two office spaces and the data acquired from these has been used to train machine learning (ML) models to learn and predict occupancy patterns. We compare the performance of K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), long and short-term memory (LSTM), and stacked LSTM (SLSTM) machine learning models on our data set. Our results show that LSTM and SLSTM outperform the other models on both data sets. Further, the trained LSTM model from one office space was tested on the data set of another office space as well as on two online UCI-occupancy detection data sets, and the LSTM model achieved an accuracy of 93.75%, 98.19%, and 99.17%, respectively. This shows that the model generalizes well over unseen data. We show that using an ML-based time-series analysis of indoor environmental parameters is effective in predicting occupancy patterns. We also study and optimize the computational complexity of the model.
Preserving Location Privacy in the Modern Era of Pervasive Environments

The rapid expansion of location-based services gives rise to significant security and privacy apprehensions. While these services deliver convenience, they accentuate concerns regarding widespread location tracking via web services, mobile apps, IoT devices, and autonomous vehicles. In this study, we comprehensively assess the merits and constraints of prevalent techniques in location privacy protection, including spatial-temporal cloaking, k-anonymity, differential privacy, and encryption. Furthermore, we delve into emerging applications like intelligent traffic planning and virus contact tracing which introduce novel complexities to the pursuit of robust location privacy safeguards.