Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Effectiveness and Efficiency of LightGBM Algorithm for Windows Malware Detection

Malware has posed a serious problem in today's world of cyber security. Effective malware detection approaches minimize damages caused by malware attack, while efficient detection strategies reduce the amount of resources required to detect malware. A previous application of LightGBM model to malware detection shows that the technique is suitable for Windows malware detection. However, the study did not compute the training time, detection time and classification accuracy of the model. There is need to evaluate the accuracy of LightGBM algorithm and determine the time required for training it. This is because quality training produces highly reliable model. It is also necessary to compute the classification accuracy and prediction time, to enhance better decision making. This paper applied the generic LightGBM algorithm on Windows malware to determine its efficiency and effectiveness in terms of training time, prediction time and classification accuracy. Performance evaluation based on the Malimg dataset shows a 99.80% training accuracy for binary class, while the accuracy for multi-class is 96.87%. The training time of the generic LightGBM is 179.51s for binary class and 2224.77s for multi-class. The classification accuracy showed a True Positive Rate (TPR) of 99% and False Positive Rate (FPR) of 0.99% for the binary classification, while the prediction time of the model are 0.08s and 0.40s for binary and multi class respectively. The results obtained for training time, detection time and classification accuracy show that LightGBM algorithm is suitable for detecting Windows malware.
A Study and Comparison of Different Sparse Bayesian Learning Algorithms in DOA Estimation

The direction of arrival (DOA) is a typical sparse parameter estimation problem. Its solution methods include greedy algorithm, norm minimization method and Bayesian estimation, in which the Bayesian methods are superior in estimation accuracy, but huge amount of computation has become the bottle-neck. This paper analyzes and compares the computation complexity of sparse Bayesian learning (SBL), multi-task sparse Bayesian learning (MSBL) and inverse-free sparse Bayesian learning (IFSBL) in DOA estimation. Simulations are also provided and prove that IFSBL is much better than SBL and MSBL in operational efficiency.
Effect of Primary VSC-HVDC Controls on the Performance of Ancillary Frequency Control Strategies

This paper analyses the impact of active and reactive power controls of VSC-HVDC on the behaviour of frequency controllers. The performance of droop and PI type frequency control strategies are investigated. The effect of feedforward and feedback active power loops are examined through bandwidth analysis. The influence of reactive power controllers on the performance is also studied. Inferences are drawn through MATLAB/SIMULINK based time domain simulations.
Deep Learning-Based Non-synchronous Measurement for Broadband Sound Source Localization

Array-based acoustic beamforming methods are prevalently used in many practical fields. However, limited by the size of the array and sensor elements, these methods cannot achieve the best performance. To mitigate the limitations, Non-synchronous Measurement (NSM), which synthesizes a virtual array with a larger aperture and denser sensor elements by shifting a prototype array to different positions, was proposed to enlarge the working frequency band of the prototype array. Since the prototype microphone array measures the sound signals without time-overlap between different measurements, some phase information of NSM is lost. Thus, the generated cross-spectral matrix (CSM) of NSM is incomplete. Although some rank-minimum CSM completion approaches recover the lost information, they rely on localization algorithms to estimate the positions of the sound sources, which is time-consuming. There-fore, we proposed a computationally friendly NSM based on deep learning to achieve effective broadband-sound source localization (broadband-SSL), which directly estimates the coordinates of the speech source. Some simulations are implemented, and the results validate the efficiency and accuracy of our proposed method.
Cascaded Convolution Neural Network for Color Image Recovery

Reconstructing the high-quality image from its degraded version has attracted more interest in recent years. This data recovery problem can be first defined as an ℓ2 norm minimization problem and then solved by deep learning techniques. In the paper, the task of color image recovery from partly observed gray scale data is tackle. It is assumed that some blocks or rectangular area of the gray scale is not observed, making the problem more complicated. The baseline convolutional auto-encoder network is first described. By dividing it into tasks of completion of missing values and image coloring, two sub-networks are proposed with similar architectures to solve the two sub-problems, and they are combined to get the final satisfying results. Experimental results shows that the proposed cascaded network can recover the image with higher PSNR and SSIM performance comparing to the baseline model.
Lowering the Power Consumption of Healthcare Centers in Bangladesh using Low-Power Consuming Computing Devices

Bangladesh's hospital receptions and wards use high-specification computers to do petty work such as patient information management, providing service information, billing and others. It is entirely unnecessary as the work performed by these computers can be done by any other low-power computing devices like thin clients or other small single-board computers. Based on this situation, the problem was identified, which was to evaluate the reduction of power consumption possible by using Low-power computing devices instead of traditional desktop computers in hospital receptions, pathology, and hospital wards. Other research studies also did a similar kind of work comparing power consumption between regular and single-board computers. Data were collected from several hospitals after visiting and observing the computer usage and hardware information used there. Alongside, the power consumption was also collected. The power consumed by a similar single-board computer, a Raspberry Pi-based system that can replace the hospital's computer system infrastructure, was also compared. It was found that a Raspberry Pi-based system can perform the same tasks consuming 4.2 times less energy than the current system. Finally, as future work, we can expand our study to other sectors like banks and offices where the existing system can be replaced with a Low-power Computing device-based system. Also, special software can be made for Raspberry Pi to optimize energy consumption further.
Dielectrophoretic separation of RBCs from Platelets: A parametric study

Dielectrophoresis is an active microfluidic separation method. This method utilizes an uneven electric field to manipulate particles. , according to their surface charge. In this numeric study. , multiple parameters affecting the separation of RBCs and platelets are addressed and investigated. It was concluded that the most important parameter. , is a design element; the channel geometry. Lower inlet velocity and higher voltage and frequency were also associated with better separation of the cells.
Fabrication and characterization of polylactic acid nanobiocomposite scaffolds containing LDH and ZIF-8 drug carrier

In the past, many people lost their lives due to superficial wound infections. Over time, the importance of wound healing as the reconstruction of the body's first defense barrier, the skin, became more important. Today, traditional medicine has given way to reconstructive medicine. Tissue engineering has always been a pioneer in the development and application of new methods as the main branch of reconstructive medicine. In this research, a Nano-Scaffold was designed and fabricated for skin Tissue engineering with unique properties using electrospinning method. In this project, to increase the function of the scaffold, two nanoparticles containing two types of drugs have been used, which is considered as a hybrid drug delivery system. Concomitant use of layered double hydroxide hybrid (LDH) nanoparticles containing curcumin and imidazole zeolite (ZIF-8) containing aspirin in poly-lactic acid scaffolding will accelerate wound healing and reduce inflammation. After processing two layered double hydroxide nanoparticles containing curcumin and imidazole zeolite containing aspirin, these two nanoparticles were loaded on poly-lactic acid nanofibers. By adding 3% by weight of nanoparticles to poly-lactic acid, the tensile strength increased from 1.31 MPa to 1.6 MPa, the contact angle increased from 59 ° to 120 ° and cell viability (within 72 hours) increased from 49% to 88%. Tests performed on the scaffold confirmed its biocompatibility.
Transfer Learning in Deep Neural Network for Land Cover Classification

Land cover classification research contributes significantly to sustainable development by enhancing the baseline for urban planning, natural resource management, and environmental monitoring at the local, regional, national, and global levels. In practice, only the RGB and near-infrared (NIR) image bands are obtainable for identifying land cover since high-resolution satellite remote sensing data may not even be accessible due to cost, storage systems, or bandwidth restrictions. This work presents Transfer Learning in Deep Neural Networks (DNN) for land cover classification, where ResNet101 is considered as a DNN model. Numerical experiments are conducted using the EuroSAT dataset, a multispectral dataset based on Sentinel-2 satellite images of 34 European cities covering 13 spectral bands in visible, near-infrared, and short-wave infrared parts. Several experiments were conducted, and the proposed ResNet101-based technique effectively classifies the land cover classes with more than 99% accuracy.
Detection of Coronary Artery Blockage at an Early Stage using Effective Deep Learning Technique

Coronary artery blockage is the most common and frequent disease affecting the human body. Our proposed method aims toward a fully automated system for detecting a coronary artery blockage at an early stage using image processing and deep learning technique that can improve medical treatment of the heart. This research aims to implement a system that can detect stenosis areas of the coronary artery due to the buildup of cholesterol, plaque, and other blocking agents using 2D angiogram images. Evaluating 2,151 angiogram image datasets, we trained and tested our models to reach a conclusion. The research uses CNN model architecture that uses a dataset of 2D angiogram images of the segmented coronary arteries, which are analyzed using VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 models. For further enhanced results, we classified our dataset in two ways; Binary Classification and Multi-class Classification. Next, using the Ensemble model architecture, we evaluated the results and accuracy of the models. According to our Confusion Matrix evaluation, our Binary Class Ensemble model detected 85 blocks and 557 no-block images. Additionally, for the Multi-class Ensemble Model, the algorithm successfully classified 10 LAD, 7 LCX, 21 RCA, 42 Multiple block images, and 557 no-block images. Finally, we achieved accuracy, precision, recall, and an F1 Score greater than 0.99 for the Binary Classification and greater than 0.98 for the Multi-class classification. In this way, deep learning techniques can improve and develop medical science at a prodigious level resulting in error-free medical treatment of the heart at an early stage.
Partial Discharge Positioning Based on SDR and TDOA

With significant advantages of large transmission capacity, long distance and high efficiency, ultra high voltage transmission technology plays an important role in optimal energy allocation and large-scale clean energy consumption. To ensure the safety and reliability of power system, it is necessary to use high-performance and reliable insulation materials and transform manufacturing process to improve insulation level and quality of high voltage electrical equipment. More importantly, it is required to monitor insulation state of high voltage electrical equipment and master operation of power system in real time. To this end, partial discharge (PD) monitoring is widely used to detect insulation defects in real time and avoid insulation breakdown. Further, this paper proposes an online positioning method based on software defined radio (SDR) for ultra high frequency PD source, which is specifically realized via an improved version of time difference of arrival (TDOA). Simulation results show that the proposed scheme achieves higher positioning accuracy than traditional method with four UHF antennas. Future work will focus on marking PD source position in actual LCD environment diagram based on FPGA.
BanglaSAPM: A Deep Learning Model for Suicidal Attempt Prediction Using Social Media Content in Bangla

Nowadays, people are constantly expressing their emotions, thoughts, opinions, and daily activities on social media. Therefore, social media posts have become a powerful tool among psychiatrists for the early detection of suicidal tendencies. However, the automatic detection of suicidal posts has become a challenging problem among researchers in the field of Natural Language Processing (NLP). A significant number of previous works have been found in the literature for the automatic detection of suicidal posts in different languages such as English. However, little effort has been devoted to automatically detecting suicidal posts in low-resource languages like Bangla due to the lack of available datasets. In this study, we have created a noble Bangla suicidal posts dataset named BanglaSPD and compared the performance of various machine learning and deep learning models for suicide attempt prediction by training and evaluating with the dataset. Finally, we have found that a deep learning-based model with CNN+BiLSTM has outperformed with 0.61 F1-Score in Fasttext word embedding methods.
Stress Detection Using PPG Signal and Combined Deep CNN-MLP Network

Stress has become a fact in people's lives. It has a significant effect on the function of body systems and many key systems of the body including respiratory, cardiovascular, and even reproductive systems are impacted by stress. It can be very helpful to detect stress episodes in early steps of its appearance to avoid damages it can cause to body systems. Using physiological signals can be useful for stress detection as they reflect very important information about the human body. PPG signal due to its advantages is one of the mostly used signal in this field. In this research work, we take advantage of PPG signals to detect stress events. The PPG signals used in this work are collected from one of the newest publicly available datasets named as UBFC-Phys and a model is developed by using CNN-MLP deep learning algorithm. The results obtained from the proposed model indicate that stress can be detected with an accuracy of approximately 82 percent.
On the spectral mapping theorem for essential spectra

"B. Gramsch and D. Lay have studied spectral mapping theorems for the essential spectra of an operator acting in a complex Banach space. Firstly they consider operators belonging to the Banach algebra of all bounded linear operators on the space, and later they derive the theorems for unbounded closed linear operators with non-empty resolvent from the aboye case ; but bounded closed linear operators with domain a proper subspace are not included. In this note we introduce a notion of extended essential spectra for any closed linear operator with non-empty resolvent, which covers the above cases. Then, in this more general context, we are able to prove the spectral mapping theorems by means of a more unified approach based on a factorization of the operators provided by the Dunford-Taylor calculus and well-known properties of products of operators present in Fredholm theory. "
Reinforcement Learning for Delay Tolerance and Energy Saving in Mobile Wireless Sensor Networks

Reinforcement Learning (RL) has emerged as a promising approach for improving the performance of Wireless Sensor Networks (WSNs). The Q-learning technique is one approach of RL in which the algorithm continuously learns by interacting with the environment, gathering information to take certain actions. It maximizes performance by determining the optimal result from that environment. In this paper, we propose a data gathering algorithm based on a Q-learning approach named Bounded Hop Count - Reinforcement Learning Algorithm (BHC-RLA). The proposed algorithm uses a reward function to select a set of Cluster Heads (CHs) to balance between the energy-saving and data-gathering latency of a mobile Base Station (BS). In particular, the proposed algorithm selects groups of CHs to receive sensing data of cluster nodes within a bounded hop count and forward the data to the mobile BS when it arrives. In addition, the CHs are selected to minimize the BS tour length. Extensive experiments by simulation were conducted to evaluate the performance of the proposed algorithm against another traditional heuristic algorithm. We demonstrate that the proposed algorithm outperforms the existing work in the mean of the length of a mobile BS tour and a network’s lifetime.
Ensure: Towards Reliable Control of Cyber-Physical Systems Under Uncertainty

Cyber-physical systems (CPSs) are complex ensembles of physical and cyber components that cooperate to offer dynamic and adaptive functionalities. Uncertainty can arise from a plethora of sources in the entangled components, ranging from the unreliable perception, the nondeterministic action effects, to even the changes in the environment. Existing controlling approaches, such as those using Markov decision process, have limited ability in handling uncertainty. To address the challenge, in this article, we novelly propose using partially observable Markov decision processes (POMDPs) to model CPS under uncertainty and show that common types of uncertainties can be modeled by partial observations and nondeterministic actions over probabilistic distributions. With POMDPs, strategies that can optimally control CPS are synthesized. We further propose a strategywise verification method, which resolves the difficult problem of verifying the entire POMDP, to offer reliable controlling strategies. Experiments on two representative cases of CPS show promising results compared with existing approaches.
RAST: Evaluating Performance of a Legacy System Using Regression Analysis and Simulation

A challenging aspect in developing and deploying distributed systems with strict real-time constraints is how to evaluate the performance of the system running in a production environment without disrupting its regular operation. The challenge is even greater when the System Under Evaluation (SUE) is a poorly documented legacy system with database-centric architecture that works within a resource-sharing environment. Current performance evaluation methods dealing with this challenge require live monitoring software or distributed tracing software tools that are typically unavailable in legacy systems and hard to establish. In this paper, we propose an alternative approach RAST (Regression Analysis, Simulation, and load Testing); it evaluates the response time as the major performance characteristic of a distributed real-time legacy system using the available system's log files. Our use case is a commercial alarm system in productive use that is provided and further developed by the GS company group in Germany. We show in extensive experiments that our approach allows to adequately estimate to what degree the workload of a legacy production system can rise in the future while complying with the strict requirements on the response time. We provide a GitHub repository with non-proprietary parts of our predictive model generation, simulation, and load testing software to reproduce our experiments.
Cyber Threat Detection Using Machine Learning Algorithms on Heterogeneous MiniVHS-22 Dataset

Large and varied amounts of data are needed for the research of emerging machine learning (ML) techniques for detecting network threats, such as malware-related threats. The research community has been using a number of network traffic datasets that have been proposed in recent years. The majority of these datasets contain, however, only a few classes of bot and malware, lacking significant diversity and generalization to identify threats. In this work, we considered a modified version of the VHS-22 dataset that we termed as MiniVHS-22. This dataset contains flow parameters extracted using a software network probe from four datasets and a network traffic malware monitoring website. Our methodology evaluates seven different machine learning techniques. More than 99% of the threats associated with malware are successfully identified by the Random Forest Classifier, Decision Tree, and Multilayer Perceptron. Additionally, we used different dimensionality reduction techniques such as the Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) with varying numbers of principal component values. Sophisticated network traffic threat detection systems can be developed using the results of our investigation.
An Ensemble Learning Approach for Chronic Kidney Disease Prediction Using Different Machine Learning Algorithms with Correlation Based Feature Selection

Chronic Kidney Disease (CKD), also known as Chronic Renal Disease is considered one of the biggest reasons acting behind deaths in adults all over the globe and the number is escalating throughout the years. At its final stages, treatment of CKD becomes much exorbitant. Machine learning algorithms, for their capabilities to learn from experience, can play a vital role in predicting CKD in its early stages. In this paper, we apply machine learning to predict CKD on the basis of clinical data obtained from the UCI machine learning repository. The dataset has a significant amount of missing values which is handled using K-Nearest Neighbours imputer. The imbalanced dataset has been balanced using Synthetic Minority Oversampling Technique (SMOTE). A Correlation Based Feature Selection (CBFS) and Principal Component Analysis (PCA) is used for feature selection. Later, the dataset is divided into 80% for training, 10% for validation and 10% for testing. Five renowned supervised learning algorithms namely K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Logistic Regression and an Ensemble learning algorithms are used to achieve the prediction. Among these, the ensemble learning algorithm proves to be superior than others on the dataset obtained by CBFS, acquiring an accuracy, precision, recall, and f1-score of 97.41%, 99.52%, 95.27% and 97.33% respectively.