Miracle-dz/UniFAR-FTUs huggingface dataset:
ANN Based Single Phase Bidirectional DC-AC Boost Inverter for Grid Connected Solar Photovoltaic Systems without a Transformer

The paper describes an artificial neural network (ANN)-based single-phase bidirectional DC-AC boost inverter for grid-connected solar PV systems without a transformer. The proposed inverter architecture effectively converts PV panel-generated DC power into AC power that can be injected into the grid. The typical boost inverter isolates the PV array from the grid with a large, costly transformer. The suggested technology is ideal for grid-connected PV systems since the inverter is smaller, cheaper, and simpler without a transformer. An ANN trained on simulated input-output data controls the bidirectional inverter. The ANN can accurately predict the grid voltage and frequency, as well as the duty cycle required for maximum power point tracking (MPPT). Using a realistic solar PV system model, comprehensive simulations were done to test the suggested inverter. Results show that the ANN-based control technique tracks the maximum power point well and stabilizes grid integration without a transformer. The suggested inverter has high efficiency, minimal THD, and quick grid disturbance response. Bidirectional power flow lets the PV system and grid exchange energy. The ANN-based single-phase bidirectional DC-AC boost inverter for grid-connected solar PV systems outperforms transformer-based inverters and costs less. The suggested design might help the grid embrace solar PV systems for clean, sustainable energy.
DASTAN-CNN: RF Fingerprinting for the Mitigation of Membership Inference Attacks in 5G

The fifth generation (5G) networks are designed to support a large range of diverse services with strict performance requirements. Studies suggest that, 5G uses machine learning technologies for variety of tasks ranging from network management, and resource optimization to automated services. The successful integration of 5G with machine learning has also led to the basis for 6G networks. However, the use of machine learning makes the 5G networks susceptible to adversarial attacks. A few works study the effect of differential privacy and adversarial attacks in the 5G systems let alone to provide the proposal of effective defense mechanism. This study proposes Denoising and Adversarial attack-based STacked AutoeNcoder (DASTAN) convolutional neural networks (CNN) to provide defense against a specific differential privacy attack, i.e. membership inference, optimized to detect the device or data distribution potentially used in the training process. DASTAN initiates an intentional attack to camouflage the characteristics of an authorized user from an adversary and uses a de noising stacked autoencoder to recover the information at service provider's end for RF fingerprinting. The aim of RF fingerprinting is to validate the authenticity and identity of the device to preserve the privacy of wireless network. Experimental results demonstrate the efficacy of DASTAN-CNN, which reduces the attack success rate by up to 52.69% in comparison to the case where no defense strategy is employed. The DASTAN-CNN also achieves 75.29% authorized user recognition rate for RF fingerprinting while reducing the attack success rate to 39.23%, which shows the effectiveness in terms of trade-off efficiency.
FPGA based ANN classifier for Bengali Handwritten Digit Recognition

In order to accurately and quickly recognize Bengali handwritten digits and characters, this paper suggests an FPGA-based hardware accelerator design of ANN for handwritten Bengali character recognition applications. To discover the best FPGA design for the various categorization rates, a study of network designs is first undertaken. Sigmoid as well as ReLU neurons integrated by Xilinx Vivado workspace and an axi DMA controller created in Verilog Hardware Description Language (HDL) make up the majority of its components. It is clear that sigmoid-based function implementation exceeds ReLU-based implementation for very tiny data widths (such as 4 and 8 bits). The maximum detection accuracy for Sigmoid implementation is 94.00%, and the maximum for ReLU is 87.00%. Finally, this hardware upgrade considerably increased processing speed by 4 times compared to software implementation and about 27 times faster in terms of existing CPU-based hardware implementation, in addition to maintaining recognition accuracy the same as software equivalence. Also, the power consumption is reduced by more than half of its existing solution as discussed before. As a result, the fast and accurate recognition of Bengali handwritten numbers with low power consumption was made possible by our specialized ANN models with FPGA capabilities.
Modeling GaN-HEMT Electrostatic Band Diagram under full depletion approximation

In this work, we propose a new approach to derive the GaN-HEMT energy band diagram under full depletion conditions. The model considers the vertical electric field and vertical potential within the GaN-HEMT from gate to bulk. The effect of polarized ions due to piezoelectric and spontaneous polarization is also considered. The spike-shaped electric field within the 2D electron gas predicted by the model follows TCAD simulation results. The model is validated by TCAD and by EPFL HEMT model under the depletion condition.
A Rapid and Simple Method for Prostate Cancer Detection Using an Electronic Tongue from Patients in Urine Samples

This work describes a simple and rapid method to detect a group of patients with prostate cancer from the acquisition of urine samples by implementing an electronic tongue composed of screen-printed carbon electrodes (C110). To validate the detection of prostate cancer samples, other samples were acquired from a group of patients called controls (i.e., healthy patients, patients with Benign Prostatic Hyperplasia and Prostatitis), which were subsequently classified using Pattern Recognition and Machine Learning methods. The results achieved were outstanding since it was possible to obtain up to 92.9% in the sample's classification accuracy.
Energy-Aware Inter-Data Center VM Migration Over Elastic Optical Networks

The rapid growth of data processing demands in large-scale data centers (DCs) has led to increased brown energy (BE) consumption, which has negative environmental impacts. Since most DCs are now powered by both BE and renewable energy (RE), migrating workloads from DCs with insufficient RE to DCs with sufficient RE can decrease the total BE consumption in the network. However, selecting a destination DC is challenging due to the uncertainty of the network and the additional cost associated with using network devices for the migration. This paper proposes to optimize the DC selection and the efficient virtual machine transfer between DCs, minimizing the costs of BE consumption, optical network devices, and migration. Specifically, we formulate the DC selection as a multi-armed bandit problem and estimate the lowest migration cost at each round using the lower confidence bound. We adopt the optical grooming technique to reduce the cost of optical devices used during the migration. We compare our algorithm with the KUBE and -Greedy algorithms on the NSFNET and show that it reduces the total cost by 4.6% and 12.8%, respectively, while having lower regret. We demonstrated the effectiveness of optical grooming by achieving a 12 % reduction in network costs.
A Comprehensive Investigation Into the Implementation of Machine Learning Solutions for Network Traffic Classification

Machine learning-based network traffic categorization has become a crucial field of study because of the rising intricacy of Internet services and the expanding usage of en-cryption. A thorough examination of the application of machine learning techniques for automated network traffic classification and analysis is given by this systematic study. A comprehensive review of the literature was done using many large scientific databases in order to find pertinent research that used machine learning methods for traffic categorization. 52 papers were chosen for final assessment out of over 700 initial results, based on relevance, rigour, and creativity. Important models, characteristics, datasets, algorithms, and limits related to traffic categorization are summarised, along with significant trends. Analysis reveals that larger traffic datasets, more feature sets, and a greater use of deep learning structures have all contributed to enhanced model resilience and accuracy. Nevertheless, problems still exist with real-time deployment, encrypted traffic analysis, and model generalisation across networks. Intelligent traffic categorization systems have the potential to improve network security, provide better quality of service, and provide insights into usage patterns, but additional advancements are necessary for real-world applicability.
Recyclable Waste Image Classification using Convolutional Neural Networks

Recycling waste is the way of engendering new materials and objects from the waste materials. Recycling helps us to prevent the waste of fresh raw materials and also saves energy and reduces air and water pollution. Every day, we produce waste that can be recyclable e.g. aluminium cans, glass bottles, paper, wood, and plastics etc. Dhaka City collects around 646 tonnes of plastic wastes every day, which is only 10% of all wastes created in Bangladesh. Only 37.2% of Dhaka’s plastic garbage gets recycled. Recycling waste is the only way to save this earth. In this paper, we have applied deep convolutional neural networks (CNN) for recyclable waste image classification. CNN is a type of deep learning algorithm particularly used for image classification tasks. We have created the waste image dataset using local wastes and applied four most popular pre-trained models MobileNetV2, VGG16, ResNet50 and EfficientNet for waste image classification. The experimental data show that MobileNetV2 obtained the highest accuracy 97.55% on the local waste image dataset.
AI and Coalition Game Interplay for Efficient Resource Allocation in D2D Communication

Fifth-generation (5G) offers more advanced and promising wireless communication technology as Device-to-device (D2D) communication. It refers to the direct data exchange between two users' equipment in a wireless network without routing their data through the base station. The close proximity of the devices offers a higher data rate with low communication latency and increases spectral efficiency. Despite the advantages mentioned above, there are still some challenges, such as interference, power control, and security, that need to be addressed concerning D2D communication. There exist many game theory-based solutions for efficient resource allocation. However, they face issues when there are many users in the communication environment. Hence, we proposed artificial intelligence (AI) and game theory-based solutions for efficient resource allocation in this paper. Initially, we proposed different machine learning (ML) classifiers, such as isolation forest (IF), support vector machine (SVM), gradient boosting (GB) classifier, K-nearest neighbours (KNN), and Gaussian naive Bayes (GNB) that select best D2D users. Then, we formulate a coalition game that gives efficiently allocates resources to the best-selected D2D users. Further, we considered different performance evaluation parameters, such as accuracy, validation loss, sum rate, and convergence rate. The empirical results represent that the GB classifier achieves the highest accuracy, 98.23%, because it trains faster with the large dataset size, and the coalition game-based approach maximizes the overall system sum rate for efficient resource allocation in D2D communication.
Unsupervised Deep Learning for an Image Based Network Intrusion Detection System

The most cost-effective method of cybersecurity is prevention. Therefore, organizations and individuals utilize Network Intrusion Detection Systems (NIDS) to inspect network flow for potential intrusions. However, Deep Learning based NIDS still struggle with high false alarm rates and detecting novel and unseen attacks. Therefore, in this paper, we propose a novel NIDS framework based on generating images from feature vectors and applying Unsupervised Deep Learning. For evaluation, we apply this method on four publicly available datasets and have demonstrated an accuracy improvement of up to 8.25 % when compared to Deep Learning models applied to the original feature vectors.
Electricity Theft Detection using Binary-Class Imbalanced Classification

Electricity theft refers to the unauthorised consumption of electricity. The energy sector faces a notable challenge of non-technical losses, which mainly arise from theft, and these losses have severe consequences for power suppliers. These effects include decreased revenue for suppliers and increased expenses for utility services for consumers. The energy supply’s stability has been compromised due to unauthorised energy consumption despite the simultaneous increase in energy production. The present circumstances significantly affect the reliability of the electrical infrastructure, leading to increased expenses for law-abiding users. Preventing unlawful energy use is the highest priority in nations like Bangladesh, particularly given the significant financial implications of energy production. This study presents a methodology that uses supervised machine learning techniques to identify non-technical losses in imbalanced datasets effectively. Several techniques are employed to address the problem of imbalanced datasets, including oversampling methods such as random oversampling, synthetic minority oversampling technique (SMOTE), support vector machine SMOTE (SVM SMOTE), and borderline SMOTE. Undersampling methodologies, namely random undersampling, cluster centroid, edited nearest neighbour, and neighbourhood cleaning rule, are also used. The present study employs several kinds of machine learning techniques, including K-Nearest Neighbour, Logistic Regression, Support Vector Machine (SVM), and Decision Tree, to analyse consumption data obtained from a sample of 56,618 records spanning the years 2017, 2018, 2019, and 2020.
Deep Reinforcement Learning Aided Rate-Splitting for Interference Channels

Rate-splitting multiple access (RSMA) has emerged as a powerful transmission scheme to mitigate interference in next generation communication systems. In this paper, we investigate precoding with RSMA for a multiple antenna interference channel based on deep reinforcement learning. Specifically, each transmitter has to effectively optimize precoders and allocate transmit power to common and private streams via multiple decision-makers with high dimensional continuous action space. In order to solve this problem, we employ a multi-agent deep deterministic policy gradient (MA-DDPG) framework. In this framework, decentralized agents have partial observability and learn a centralized critic with a multi-dimensional continuous policy. Simulation results show that the proposed rate-splitting method achieves the information theoretical sum-rate upper bound for the single antenna case. It also has a performance very close to the upper bound for the multiple antenna case and outperforms MA-DDPG with no rate-splitting, maximal ratio transmission, zero-forcing and leakage-based precoding methods in both cases.
Pediatric Bone Age Prediction Using Deep Learning

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child’s growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach’s efficacy in pediatric endocrine disease diagnosis.
pic2code: Generating HTML Code from Handwritten Picture

The recent development of automated code generation is an expanding area of investigation within the realm of research. In the realm of User Interface code design, a considerable amount of attention has conventionally been devoted to the meticulous creation of visually appealing designs. This research presents an innovative technique that has been developed to effortlessly convert hand-drawn sketches into hypertext markup language code. This breakthrough enables anyone with limited experience to easily create HTML websites using basic diagrams. The methodology used in this study is based on the highly acclaimed YOLOv3 model, which is widely recognized for its exceptional object detection capabilities. In the methodology, a deep convolutional neural network algorithm has been carefully integrated into the Sketch2Code data set. This procedure guarantees that our model not only attains notable efficacy but also regularly produces precise outcomes. Through extensive evaluations, the accuracy rate of our suggested system is found to be 87.71%, as measured by Mean Average Precision. In the current period characterized by the significance of online development, the ability to automatically convert designs into HTML code streamlines the process of constructing web interfaces, while enabling designers and developers to explore novel opportunities.
Superposition Transmission of Common Alarm Information in MIMO GF-NOMA

In this paper, we study the coexisting scheme including common alarm messages with correlated user activity and standard messages with uncorrelated user activity in the multipleinput multiple-output grant-free non-orthogonal multiple access. Concretely, all the potential users are divided into multiple groups each monitoring an additional alarm event besides the standard message transmission. Active users transmit the superposition of standard messages and alarm messages if the alarm events are triggered and detected, and transmit only standard messages otherwise. Moreover, the alarm message is transmitted by utilizing the principle of index modulation. At the base station, the Turbo approximate message passing algorithm is designed to realize the joint user activity detection, channel estimation, and alarm information decision. Finally, computer simulations are provided to verify the effectiveness of the proposed scheme.
Low-Complexity Fingerprint Construction for RIS Codebook Design in Millimeter Wave Systems

Configuring the reflection coefficients of a reconfigurable intelligent surface (RIS) is essential in harvesting the RIS benefits. When using beam training methods for the configuration, a large-sized codebook is required to reflect signals in all possible directions, incurring high beam training overheads. To address this issue, this paper considers a RIS beam training system operated in millimeter wave (mmWave) bands and proposes a fingerprint-based technique to reduce the codebook size using prior knowledge of the channel. The fingerprint database tensor records signal powers received at different positions and RIS reflection coefficients so that one can recommend a small-sized codebook for the RIS beam training. To reduce the fingerprint construction overhead, we exploit the mm Wave channel sparsity and channel spatial consistency to propose a sparse sampling strategy, which collects fingerprint data in only 35% of positions and 10.75% of codewords under the signal-to-noise (SNR) = 15dB. The other unsampled data is inferred by a formulated tensor completion problem. To efficiently solve the problem, we develop the conjugate gradient (CG) method to accelerate the alternating direction method of multipliers (ADMM), reducing 80.40% of running time. Numerical results show a similarity between the completed tensor and the benchmark tensor acquired in full sampling. The size of the recommended codebook is 2% of the full codebook, and the optimal achievable rate is approached by the recommended small-sized codebook.
Low-Cost Network Measurement Through Intelligent In-Band Network Telemetry Orchestration

Recently, diverse emerging scenarios have precipitated a substantial surge in the variety of devices and applications, which has consequently imposed more stringent demands on Quality of Service (QoS) prerequisites. As a burgeoning emerging network measurement method, In-band network telemetry (INT), can provide detailed metrics for QoS by obtaining fine-grained network status information. However, INT only outlines device-level operations, which fails to provide an entire network view for monitoring. To address this, INT orchestration based on network topology and application requirements to achieve network-level monitoring is necessary. In this paper, we propose an INT orchestration model that efficiently measures the entire network while minimizing measuring overhead. The model outputs the probe path and collects requirements for the devices it passes through. Our method effectively reduces network bandwidth consumption caused by INT process and ensures telemetry items remain fresh. Experiment results support the effectiveness of our approach.
A Multi-Task Approach for Real-Time Quality of Experience Factors Prediction from Physiological Data

In the multimedia field, the quality of experience (QoE) is rightfully seen as the center metric in research, around which each piece of the network is designed, especially for next-generation networks. Even if an increasing number of models using various data as inputs are now available, some major problems remain, such as the implementation of quality of experience measurement for real-time applications. The 'Human Factors' are the primary reason for the impossibility to correctly predict the QoE for real-time applications since these factors can't be measured easily and swiftly. For this reason, we present in this paper a Multi-Task model to predict multiple QoE influence factors at once from physiological data to save time in the training process and during the prediction. To test the model, we use a publicly available dataset named SoPMD, which contains recordings from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiratory signals obtained during a quality assessment experiment, where QoE factors are gathered as labels. Our Multi-Task model has been tested using different features extracted from EEG and presents results up to 68.51% in accuracy. This model could be used in a real-time regulation loop to predict QoE factors faster than single-task models, for an enhanced QoE prediction, as this model can predict the five factors at the same time.