Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Brain Tumor Detection using Deep Learning Convolutional Neural Networks (CNN) for Medical Imaging Analysis

Tumours in the brain can vary greatly in size, shape, and colour. In a single human brain, it is possible to find more than one type of tumour. Careful removal of the tumor-afflicted region is the first step in treating brain tumours. When compared to non-deep learning methods, the Deep Learning Framework's wealth of interesting ideas suggests that it would be better at segmenting regions of the brain associated with anxiety. To improve the accuracy of tumour detection in MRI scans, the study suggests using updated versions of traditional CNN models. The model's performance in identifying tumours surpasses baseline methods after training on a massive collection of MRI images. Brain tumours can be detected earlier with the proposed CNN models, leading to better treatment results and potentially saving lives. All things considered, the findings point to the possibility of deep learning techniques improving medical picture processing and disease identification. Using a different combination of kernel, filter, convolutional layer, batch, and fully connected layers, the proposed method employs six different CNN models. The suggested models have been evaluated in comparison to other convolutional neural network (CNN) models, such as CNN Model 1 (which has a training accuracy of.9984 and a testing accuracy of.9687), CNN Model 2 (which has a training accuracy of.9716 and a testing accuracy of.9624), CNN Model 3 (which has a training accuracy of.9961 and a testing accuracy of.9832), CNN Model 4 (which has a training accuracy of.9992 and a testing accuracy of.9878), CNN Model 5 (which has a training accuracy of.9994 and a testing accuracy of.9886), and CNN Model 6 (which has a training accuracy of.9996 and a testing accuracy of.9809).
Advanced Techniques in Network Traffic Analysis: Utilizing Wireshark For In-Depth Live Data Packet Inspection And Information Capture

The escalating frequency and sophistication of cyber-attacks have placed a spotlight on the importance of Quality of Service (QoS) and robust network security mechanisms. Effective traffic analysis and distribution are critical for maintaining the integrity of network applications and safeguarding data. Our research focuses on the development and assessment of a network intrusion detection system (NIDS) that utilizes the advanced packet analysis capabilities of Wireshark, a renowned network protocol analyzer. The system is designed to enhance network security by enabling more efficient and accurate data collection through network monitoring tools. This serves as the foundation for detecting and thwarting a wide range of malicious activities, including malware and spyware incursions, within network traffic. The innovation of our approach lies in its integration of packet sniffing technology with AI-driven analysis techniques. By applying Machine Learning algorithms to the data captured by Wireshark, our NIDS is capable of identifying patterns indicative of intrusive behavior, which might elude traditional detection methods. We present a comprehensive evaluation of our system, showcasing its efficacy in real-time intrusion detection and its potential for integration into existing network infrastructures. This study contributes to the ongoing discourse in cybersecurity, offering a sophisticated tool in the battle against digital threats and reinforcing the role of intelligent systems in protecting network environments
Max-Min Rate Optimization in Cell-Free Massive MIMO Systems Using RSMA Scheme

In order to reduce the inter-user interference of the system and increase the throughput of each user, this paper proposes a cell-free massive MIMO system using rate splitting multiple access (RSMA) scheme. In particular, an max-min rate problem is formulated by optimizing the beamforming at access points and rate allocation, and a path following algorithm is proposed to obtain the optimal solution to the problem. Simulation results demonstrate the effectiveness of the proposed model and algorithm in improving the system throughput.
A Comparative Analysis of Classification Methods Using Oversampling Methods for Diabetes Dataset

Diabetes is one of the most common chronic diseases that raise blood sugar. In case diabetes is untreated and undiagnosed, it can lead to many adverse effects. We proposed a new clustering-based hybrid approach to diagnose diabetes using a publicly available PIMA dataset. The proposed algorithm uses the affinity propagation algorithm to obtain K, the optimal number of clusters for k-means clustering. K-mean clustering is used for informative majority sample selection. The oversampling method, RWO, is further used to rebalance the training set, and KNN is used as a classifier for the proposed approach. This paper also presents a comparative analysis of six oversampling methods for the early detection of diabetes. We have used six classification methods, SVM, Logistic Regression, Random Forest, XGBoost, KNN, and Decision Tree, as classifiers to train the data. ROSE, SMOTE, BLSMOTE, RWO, ADASYN, MWMOTE, and DBSMOTE are used for oversampling. Different performance metrics Recall, Specificity, Precision, F1-score, Accuracy, and AUC, are used to present the classification results of experiments. The proposed method outperforms other oversampling methods with Specificity 84 %, Precision 73.8%, Recall 84.9%, F1-score 79%, Accuracy 84.31 %, and AUC 88.3%.
Modelling of BLDC Motor FOC and Hardware Implementation Using F28069M LaunchPad

Many applications use brushless DC (BLDC) motors because of their great efficiency, torque density, and dependability. BLDC motors can be precisely controlled with the help of a sophisticated control method called field -oriented control or FOC. This project uses the F28069M LaunchPad to model and implement FOC for a BLDC motor using Model-Based Design (MBD). Using MATLAB/Simulink, the BLDC motor model is created and then verified against experimental data. Using Simulink Coder and MBD, the FOC controller is implemented on the F28069M LaunchPad. The three-phase stator currents and voltages are transformed to the d -q reference frame using the Clarke and Park transformations, which constitute the basis of the FOC controller. To get the required torque and speed out of the motor, a proportional-integral (PI) controller is then used to regulate the currents in the d- and q-axes. The rotor position of the BLDC motor is determined by the MBD implementation of the FOC controller using the rotor position estimation approach. Accurate control of the d- and q-axis currents is necessary for the implementation of FOC, and this requires the rotor position estimation technique. Experimental methods are used to assess the FOC controller's performance. The outcomes demonstrate that the FOC controller can precisely control the BLDC motor's torque and speed. Accurate rotor position estimation can also be achieved with the use of the rotor position estimation approach. Researchers and engineers interested in learning about and utilizing FOC for BLDC motors through MBD will find the project to be a useful resource.
Fusion of Enhancing Target Features and Focusing Slice Features for Soil Image Classification

Soil classification based on the identification of soil surface and physical properties is a key point in calculating indicators such as stability, bearing capacity, and uniformity of construction sites in soil exploration. In this paper, soil image classification is regarded as a sub task of fine-grained image classification, and a soil image classification algorithm model integrating target feature enhancement and slice feature focusing is proposed. This model constructs a three branch network for Supervised learning. First, the backbone network obtains the overall characteristics of the image; Then, the image position is obtained through cross level feature fusion to reduce background noise interference and enhance target features; Finally, based on the feature mapping relationship of the target image, a local region with the highest degree of differentiation and the lowest degree of redundancy is proposed to achieve slice feature focusing. The proposed model has achieved competitive results on CUB-200-2011, Stanford Cars, FGVC-Aircraft and Soil.
Enhancing Locational FDIA Detection in Smart Grids: A Hyperparameter Optimization Analysis

In cyber-physical power systems, mitigating the detrimental effects associated with the stealthy nature of False Data Injection Attacks (FDIAs) is imperative. This paper introduces a deep learning model based on a 1-D Convolutional Neural Network (1-D CNN) for simultaneously localizing and detecting FDIAs. To enhance the predictive accuracy of the CNN model, the hyperparameters must be tweaked. An analysis focused on hyperparameter optimization is performed by comparing various optimizers, including random search, hill climbing, and Bayesian Optimization (BO). To this end, the number of filters, kernel size, and number of layers of the CNN structure are optimally tuned using Graphics Processing Unit (GPU) resources. The findings of this study present evidence that the BO method is perfectly tailored for the hyperparameter tuning task with minimum error in comparison with competitive models. The efficiency of the CNN-BO method is demonstrated with two benchmark representative examples from the IEEE 14-bus system and the IEEE 118-bus system. For the IEEE 14-bus system, BO uses 256 filters, five kernels, and four layers; the IEEE 118-bus system uses 128 filters, three kernels, and eight layers. The CNN-BO method outperforms other optimization algorithms by achieving an impressive locational detection accuracy of 96.67% for the IEEE 14-bus system.
Assessment of Convolutional Neural Networks for Asset Detection in Dynamic Automation Construction Environments

Integrating social robotics into the construction industry, particularly in the context of Industry 5.0, faces several challenges in creating complex environments that seamlessly blend human and machine interactions. In this regard, the emergence of intelligent and expert systems holds promising technologies to enhance construction tasks focused on robots and workers in 3D printing applications. This work compares several methods of convolutional neural network-based object detectors designed to identify distinct construction assets and workers within the dynamic environment of 3D printing. To this end, different versions of the You Only Look Once v8 (YOLO v8) algorithm have been implemented, trained, and experimentally tested using several images captured within dynamic construction environments. Furthermore, we present an in-depth comparison between YOLO v8 and its preceding versions, namely YOLO v7 and YOLO v5. Experimental results disclosed the high performance of the proposed approach in effectively detecting three distinct entities (workers, robotic platforms, and building elements), achieving a precision rate of up to 98.8%.
Stacked Ring-Slit Dual-Band Broadband Circularly Polarized Antenna for Satellite Navigation

This paper presents a dual-band broadband circularly polarized antenna applied to Chinese Compass Navigation Satellite System (COMPASS). It is based on the study of circularly polarized ring antenna and combined with the eigenmode theory analysis, the frequency band is broadened and complex feed networkby is simplified by using a stacked and the circular polarization characteristics can be achieved in both frequency bands. The measured impedance bandwidth of this antenna is 1.195-1.235GHz and 1.495-1.665GHz, and the maximum in-band gain is 6.6dB, which can be applied to two civil signals B1c and B2b in COMPASS.
High - Isolated MIMO Antenna Design Based on Polygonal DGS

This paper presents a novel decoupling method utilizing polygonal defect ground structures (PDGS). The PDGS boundary is defined by an “I” shaped region, which is further divided into compact small rectangles. The height of the rectangles in the DGS is then adjusted using a genetic algorithm (GA). Subsequently, an antenna is designed on a 20 mm × 30 mm FR4 substrate, with a 0.051 edge-to-edge distance between antenna elements. According to simulation data, this antenna has a good mutual coupling of -49.60 dB, an envelope correlation coefficient (ECC) of 0.001, a diversity gain (DG) of 9.99 dB, which is very close to the ideal value of 10 dB, and a radiant peak gain of 4.55 dBi. This decoupling method is certainly applicable to a variety of MIMO antenna designs.
Stock Market Price Trend Prediction using an Outlier Data Mining Algorithm

The stock market is an area where the stock prices are not constant; however, they are dynamic, nonlinear and volatile in nature. The prediction of the stock price is a challenging task with the factors such as the financial performance of the organization, politics, unexpected natural and man-made causes, and worldwide economic conditions. In concern to these issues, several analytic techniques are being developed by researchers, financial analysts, and data scientists for exploring the nature of stock market trends a novel approach is known as the Heuristic optimization-based hybrid approach for the pre-processing of data collected from the stocks. The GANN square technique for the prediction of stock price exactly. The data pre-processing involves four stages (i) ignoring the data with missing values of features, (ii) data imputation initialization, (iii) detection and deletion of outliers, and Hybrid Archimedes-based SALP Swarm Algorithm based future selection. Thus the stated approach effectively removes the unwanted noises and features from the dataset. this made it easier for the GANN square technique the prediction of stock prices effectively
Empowering Intelligent Manufacturing with the Potential of Edge Computing with NVIDIA's Jetson Nano

Manufacturing must use technology to meet demand since it is crucial to the supply chain. Particularly, artificial intelligence has been essential in helping to oversee and support the manufacturing process. This study puts forth the notion of how edge computing aids manufacturing. This article uses NVIDIA's Jetson Nano to address the demand for edge computing in preventing resource overstocking and understocking in the production unit. The concept of quality inspection is examined, as well as the quality inspection of the goods received in the production facility and its effective management through the use of image recognition. The paper primarily focuses on the implementation of deep learning and machine learning models on edge devices. By striking a balance between the computational capacity and memory limits, a domain-specific technique is applied for edge computation in the manufacturing industry employing artificial intelligence networks.
Trust, Privacy and Security Aspects of Bias and Fairness in Machine Learning

In today's world, an increasing number of decisions are being affected by machine learning (ML) algorithms in critical contexts ranging from banking to healthcare, recruitment, education, and criminal justice. Since ensuring fair and unbiased outcomes in these contexts is imperative, a large body of recent work has focused on bias and fairness in ML. In this paper, we consider the trust, privacy, and security aspects of bias and fairness in ML. From the trust aspect, we argue that for fairness measurements to be robust and trusted, a diverse set of fairness metrics should be consulted, and the agreements and disagreements between them should be well-understood. Upon conducting an empirical study with ten fairness metrics, three datasets, and three correlation notions, we identify fairness metrics that are positively correlated, negatively correlated, and uncorrelated by nature. From the privacy aspect, we investigate the impact of differential privacy (DP) on ML models and find that current differentially private ML mechanisms suffer from two drawbacks: reduced accuracy and increased bias. From the security aspect, we propose a backdoor attack to inject bias into NLP models. Upon experimentally testing our attack, we observe that modern transformer-based NLP models (such as BERT and RoBERTa) are more vulnerable to our attack, our attack is able to remain stealthy, and it can generalize to dynamic (changing) triggers presented at test time. Overall, our work highlights the intersections between two research directions that are often studied independently: (i) trust, privacy, and security in ML, and (ii) bias and fairness in ML.
A W-Band Dual-Band Filter Based on Dual-Mode Resonators

With the rapid development of broadband and high-speed wireless system, multiband bandpass filters become essential front-end modules. In this paper, a compact W-band dual-band bandpass filter is developed based on three high-order mixed-mode resonators. $\text{TE}_{301}$ and TEI02 modes are selected as the operating modes of the dual-mode resonators after systematic analysis. The coupling between adjacent resonators is controlled using a H-plane iris. The entire design of the dual-band filter is simple. Besides, the resonator based on $\text{TE}_{301}$ and TE102 high-order modes could allow for higher fabrication errors compared with single-mode resonator. Finally, an experimental prototype working at 80 and 100 GHz is designed with 3-dB fractional bandwidths (FBWs) of 3.27% and 3.76%. Compared with the reported dual-band filters, the simplified structure and fabrication advantage are highlighted.
Analysis and Identification of Malicious Mobile Applications

The proliferation of mobile applications in the digital landscape has led to an increasing threat of malicious software targeting smartphones. This research paper provides a comprehensive study on the analysis and identification of malicious mobile applications. The paper covers a thorough examination of different types of mobile threats, methodologies for analysis, and presents a novel approach for the accurate identification of malicious applications. The proposed methodology combines static and dynamic analysis techniques to enhance the accuracy and efficiency of detection.
Influence of Process Energy on the Formation of Imperfections in Body-Centered Cubic Cells with Struts in the Vertical Orientation Produced by Laser Powder Bed Fusion from the Magnesium Alloy WE43

The low specific density and good strength-to-weight ratio make magnesium alloys a promising material for lightweight applications. The combination of the properties of magnesium alloys and Additive Manufacturing by the Laser Powder Bed Fusion (LPBF) process enables the production of complex geometries such as lattice or bionic structures. Magnesium structures are intended to drastically reduce the weight of components and enable a reduction in fuel consumption, particularly in the aerospace and automotive industries. However, the LPBF processing of magnesium structures is a challenge. In order to produce high-quality structures, the process parameters must be developed in such a way that imperfections such as porosity, high surface roughness and dimensional inaccuracy are suppressed. In this study, the contour scanning strategy is used to produce vertical and inclined struts with diameters ranging from 0.5 to 3 mm. The combination of process parameters such as laser power, laser speed and overlap depend on the inclination and diameter of the strut. The process parameters with an area energy of 1.15–1.46 J/mm2 for struts with a diameter of 0.5 mm and an area energy of 1.62–3.69 J/mm2 for diameters of 1, 2 and 3 mm achieve a relative material density of 99.2 to 99.6%, measured on the metallographic sections. The results are verified by CT analyses of BCCZ cells, which achieve a relative material density of over 99.3%. The influence of the process parameters on the quality of struts is described and discussed.
Unsupervised Learning in a Ternary SNN Using STDP

This paper proposes a novel implementation of a ternary Spiking Neural Network (SNN) and investigates it using a hierarchical simulation framework. The proposed ternary SNN is trained in an unsupervised manner using the Spike Timing Dependent Plasticity (STDP) learning rule. A ternary neuron is implemented using a Dual-Pocket Tunnel Field effect transistor (DP-TFET). The synapse consists of a Magnetic Tunnel Junction (MTJ) with a Heavy Metal (HM) underlayer, allowing for the adjustment of its conductance by directing a current through the HM layer. Further, we show that a pair of dual-pocket Fully-Depleted Silicon-on-Insulator (FD-SOI) MOSFETs can be utilized to generate a current, which reduces exponentially with increasing duration of firing events between pre- and post-synaptic neurons. This current modulates the synapse’s conductance according to STDP. Furthermore, it is demonstrated that the proposed ternary SNN can be trained to classify digits in the MNIST dataset with an accuracy of 82%, which is better (75%) than that obtained using a binary SNN. Moreover, the runtime required to train the proposed ternary SNN is $8\times $ less than that required for a binary SNN.
What motivates single women to migrate from northern Ghana to Accra?

ABSTRACT In recent decades there has been a large migration stream of single women from the north to Accra in Ghana. Existing studies have focused on young migrant women’s livelihood strategies in their place of destination. However, once-married women – divorced and widowed women and neglected wives – also migrate, and their motivations for migration are less known. Drawing on qualitative and quantitative methods, the authors investigate the effects of gender norms, age, marital status, socio-economic status, and position in households on women’s decisions to migrate. The results revealed that migrant women from resource-poor households, regardless of age, marital status and position in households, commonly cited a gain in autonomy as an important motivation for their migration. From a differentiated perspective, young unmarried women aspired to prepare themselves for often expensive religious marriage ceremonies, whereas once-married women invest in their children’s education and build their own housing. By paying attention to the effects of gender norms, age, marital status, socio-economic status, and position in households on women’s decisions to migrate, the study illuminates the contradictory ways in which their migration practices are both shaped by and shape gender ideologies in parts of contemporary northern Ghana.
Naive geography and geopolitical semiotics: The semiotic analysis of geomental maps of Russians

Abstract This article is devoted to the study of naive geopolitical pictures of the world. The material of the study is “naïve” maps of participants from several regions of Russia. A semiotic approach was used on the description of the materially-produced results based on the analysis of two codes: graphic (location, toponomas, size, etc.) and verbal (verbalized representations). The study provides new knowledge about the importance of the regional factor, the ratio of the natural and social, the correlation of one’s own and somebody else’s world, etc., in ordinary consciousness.