Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Imbalance and Ensemble Learning Methods for Improved Load Prediction in Cloud Computing Systems

Load prediction is a critical component of effective resource management in cloud computing. It ensures optimal performance and efficiency by anticipating overload, underload, and normal load periods. However, achieving accurate predictions remains challenging due to the highly dynamic and often non-linear workload patterns typical in cloud environments. Traditional methods, while helpful, have shown limitations in handling these complexities. Machine learning techniques, specifically imbalance and ensemble learning, have shown potential for improving prediction accuracy. Imbalance learning addresses the uneven distribution of load states, while ensemble learning combines multiple models to achieve better predictive performance. It is possible to create a more robust and accurate load prediction system by leveraging these two methods. This paper explores the application of imbalance and ensemble learning to improve load prediction in cloud computing systems. Through an experimental study, we illustrate how these techniques outperform traditional methods, offering potential improvements to the performance and efficiency of cloud computing operations.
Deep Reinforcement Learning Based Energy Efficiency Maximization Scheme for Uplink NOMA Enabled D2D Users

Device-to-device communication (D2D-C) is an leading edge technique in 5G and forthcoming 6G networks due benefits for enhanced spectrum efficiency and energy-efficiency (EE). Despite these potential advantages, co-channel interference (CO-CI), cross-channel interference (CR-CI), and massive connectivity are the major issues in D2D-C. In order to handle these issues, an interference mitigation technique for D2D mobile groups (D2Gs) utilizing up-link Non Orthogonal Multiplexing (NOMA) is presented to improve the EE of the overall network. D2Gs boost the SE by sharing the sub-channels (SCs) to cellular users (CUs), and NOMA links a huge number of D2D users (DUs) to D2D transmitters (DT). The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with associated SCs and power restrictions of the CUs and DUs. A deep reinforcement learning (DRL) based distributed deep deterministic policy gradient (D3PG) approach is considered to enhance the EE by addressing the resource allocation and power control of DUs. Numerical outcomes showed that the suggested scheme overcomes state-of-the-art techniques in terms of results.
Blockchain oriented Spectrum Detecting and Sharing Auction Paradigm for 6G Wireless Networks

By the proliferation of huge portable electronics, frequency sources have grown more and more rare. The way to successfully and safely employ the restricted radio spectrum has grown into a basic issue in prospective wireless communication systems. By concentrating on intelligent detecting and sharing, this paper recommends a blockchain oriented spectrum detecting and sharing and auction technique (BoSDSA), that selects sufficient base stations in order to create a consortium using blockchain technology to ensure an a safe and effective spectrum the auction with low intricacy. Initially, we propose a reverse auction oriented reward framework (RARF) to provide buying strategies for the primary users and secondary users choosing the primary user that optimizes the utility. Secondly, a unit utility oriented bidding algorithm (UUoBA) is suggested for achieving a stable match among primary users and secondary users. Primary users will pick the secondary user with the highest unit efficacy in terms of utility to finish the bidding paradigm. Eventually, the transaction records are made into blocks and transmitted to ensure the security of deals. Finally, we draw the comparison of proposed paradigm with conventional techniques, the suggested BoSDSA improves the overall price and output of secondary users by 212.8% and 190.2%, correspondingly.
Predicting the cooling energy requirement for an inland building in South Africa

Energy consumption contribution of heating and cooling of a building has been on the rise in recent times. In this paper, the cooling demand of an inland building in South Africa is predicted. Hourly historical data of weather and cooling energy was collected from Ninja for the year 2019 and subsequently analysed using Julia programming language. Initially, the pattern of features is analysed to establish a picture of their behaviour. These features are then used to train and test a regression model. The analysis shows that the data collected depicts accurately the situation in the inland of South Africa for cooling power requirements. Results of the regression analysis show that there is a need to segregate the data to enhance the accuracy of the model.
A Practical Framework for Designing and Deploying Tiny Deep Neural Networks on Microcontrollers

For many applications, Deep Neural Networks (DNNs) trained on powerful CPUs and GPUs are expected to efficiently perform inference on tiny devices. However, deploying productively un-constrained complex models to microcontrollers (MCUs) remains a time-consuming task. In this paper, a comprehensive methodology is presented that combines advanced optimization techniques in hyperparameter search, model compression, and deployability evaluation using benchmark data.MCUs typically have low-power processors, limited embedded RAM memory and FLASH storage, providing orders of magnitude fewer computational resources than what cloud assets offer. Designing DNNs for such platforms requires effective strategies to balance high accuracy performance with low memory usage and inference latency. To address this challenge, Bayesian optimization has been applied, a powerful complexity-bounded technique, to hyperparameter tuning to select tiny model architecture candidates. Several pruning and quantization methods have been developed to compress all the models and evaluated the numerical performance after compression. Additionally, cloud-based deployment tools have been utilized to iteratively validate the on-device memory and latency performance on off-the-shelf MCUs. Through evaluating the benchmarks against the stringent requirements of tiny devices at the edge, practical insights have been gained into these models.Multiple image classification applications have been applied on a variety of STM32 MCUs. The practical framework can: a) maintain top-1 classification accuracy within tolerance from the floating-point network after compression; b) reduce memory footprint by at least 4 times; c) reduce inference runtime significantly by avoiding external RAM usage; d) adaptable to many different applications.
ON- and OFF-State Performance of Normally-OFF β-(AlxGa1-x)2O3/Ga2O3 MODFETs with p-GaN Gate

This article presents a systematic investigation into the impact of different device parameters on the performance of normally-OFF β-(Al<inf>x</inf>Ga<inf>1-x</inf>)<inf>2</inf>O<inf>3</inf>/Ga<inf>2</inf>O<inf>3</inf> modulation doped field-effect transistors (MODFETs) with p-GaN gate. The analysis examines the performance of this heterostructure device in both the ON-state and OFF-state. Special emphasis is given on evaluating the ON-resistance, threshold voltage, driving capability, OFF-state breakdown voltage, and power figure of merit (PFOM) of the device. For this purpose, a well-calibrated TCAD simulation tool is used. The findings of this study offer valuable insights into the design of high-power Ga<inf>2</inf>O<inf>3</inf>-based MODFETs, and can also serve as a useful reference for future development in β-(Al<inf>x</inf>Ga<inf>1-x</inf>)<inf>2</inf>O<inf>3</inf>/Ga<inf>2</inf>O<inf>3</inf> MODFET technology.
Design of Miniature Automatic Driving Vehicle Controlled by Camera Image Processing

We are developing a miniature self-driving car for the design contest at ICCE2024, which will be driven automatically by image processing of camera images. Our implementation uses a AMD/Xilinx SoC FPGA as the central controller. The hardware of the programmable logic part of the SoC FPGA is used for object detection in the camera image and PWM control of the motors. Other processes are software-controlled by the processor of the SoC FPGA.
Rapid Peanut Dryer

A quick drying process for peanuts is very important to maintain the quality of the peanuts and to extend its shelf life. Conventional drying processes often involve extended drying times, resulting in decreased productivity, increased energy consumption and a higher risk of quality deterioration. These challenges significantly impact peanut farmers, processors and the overall peanut industry. In order to overcome these problems, the Rapid Peanut Dryer is designed with a closed loop system aimed at achieving efficient and accelerated peanut drying. The closed system performance evaluation focused on key parameters such as drying time and energy efficiency. The closed system significantly reduced drying time compared to traditional methods due to controlled air flow and optimized heat distribution. The combination of airflow and heat effectively promotes the evaporation of moisture from the peanuts, making them dry quickly. In addition, the use of industrial electric fans (DC 12V) and electric heaters has improved energy efficiency, minimizing energy consumption while maintaining optimal drying conditions during the entire drying process of the peanut. The success of the system is augmented by the utilization of an Arduino Uno microcontroller which is essential for switching the fan, heater, and motor via a relay (5v DC), thereby optimizing system control and efficiency. The designed closed system provides an efficient and effective solution for drying peanuts quickly. The system significantly reduces drying time by incorporating controlled airflow and temperature control while preserving peanut shell life and nutritional value. The system will contribute to the advancement in groundnut drying process by minimizing the power consumption and provide practical insights in reducing the peanut drying time.
Privacy Computing with Right to Be Forgotten in Trusted Execution Environment

Sharing private data is at risk of potential data breaches, including the violation of the “right to be forgot-ten” principle, undermining people's willingness to share their data. A common solution is to involve the Trusted Execution Environment (TEE), which allows the data provider to verify the computation process without trusting others. However, previous works have either encountered incomplete computations or lacked scalability. In this paper, we propose TEERASE, a secure data-sharing framework that addresses these issues. TEERASE protects every phase of the data lifecycle and enables individuals to share personal data with a predefined privacy budget. In particular, TEERASE applies comprehensive privacy budgeting mechanisms to efficiently manage privacy budgets and employs an asynchronized execution approach that decouples budget consumption from data computation. TEERASE records the predefined privacy budgets, verifies privacy consumption requests, updates the remaining budgets, and deletes data that have exhausted their budgets by preventing any attempts to access them. We implement a prototype of TEERASE and evaluate its effectiveness with a realistic case study on Genome-Wide Association Study.
Improved Brain Tumor Classification Made Possible Through the Use of Transfer Learning Model ResNet50

This study gives a detailed assessment of the performance of a classification model in the context of brain tumor classification, employing a dataset consisting of four separate classes: glioma tumors, meningioma tumors, normal brain tissue, and pituitary tumors. The results of this evaluation are presented in the form of a comparison. The model, which was constructed using transfer learning using the architecture of ResNet50, has been subjected to a stringent evaluation based on accuracy, recall, and F1-score for each class. The findings reveal high classification skills, with considerable accuracy, recall, and F1-Score value distributions across all classes, suggesting successful tumor type discrimination. The model's ability to accurately classify brain tumors is shown by the high overall accuracy score of 91%. In addition, macro and weighted averages provide additional evidence that the model's performance is well-balanced when taking into account class distribution. These discoveries contribute to the progress of medical image analysis, which promises more accurate and quick detection of brain cancers, leading to improvements in patient care.
Using Support Vector Machine to Detect and Classify the Alzheimer Disease

Alzheimer's disease is a neurological condition that gradually affects memory, thinking, and reasoning abilities as well as daily functioning. The majority of people with this condition are elderly, particularly those over 65. The exact cause of this disease is still unknown, but it is now thought that it may be inherited, unintentional, or brought on by other factors. Actual assessment, CT examines, X-ray sweeps, and positron emission tomography, are a couple of techniques to analyze the disease, but X-ray checks are the most widely recognized one. Additionally, scanned images from MRI scans are used in this study. This study has established four groups for the condition: healthy, mildly demented, moderately demented, and very mildly demented. The most famous SVM technique is utilized in our proposed model along with a profound learning calculation because of its fast, trustworthy, and compelling activity. The dataset was obtained from Kaggle and uniformed through pre-processing. After the information was parted into preparing and testing datasets in a 80:20 proportion, tuner advancement was done to naturally pick various boundaries to improve the model. The ailment and its classification are then resolved utilizing the SVM grouping and the model's exactness is 99.4%. In order to diagnose the patient's condition and discover an appropriate course of treatment and therapy, it helps the doctors identify the disease and the degree of its dissemination.
A Realistic Simulation Method for the Micro-Doppler Characteristics of Helicopter Rotor Blade Radar Echo

Aiming at the problem that the simulation results are inconsistent with the actual results due to the simplified effect of rotor blade bending on echo in helicopter modeling, based on the scattering and dynamic characteristics of rotor blades, the influences of blade bending and hub rotation on modeling are analyzed. By simulating the position information of each scattering point, a refined model of helicopter radar echo is constructed. The fretting characteristics of radar echoes are analyzed theoretically. The simulation results verify the correctness and feasibility of the theoretical analysis. The establishment of this model improves the theory of rotor blade echo model, and has certain theoretical and application value in the field of detection and recognition of rotor targets.
Assessing the Efficacy of Reinforcement Learning in Enhancing Quality of Service in SD-WANs

The continuous growth of advanced services integrating processing, storage, real-time data exchange, and transmission capacities highlights the importance of Quality of Service (QoS) and Quality of Experience (QoE). In particular, it is very relevant for enterprises that rely on Wide Area Networks (WANs) for reliable and efficient communication between their headquarters and branches. With the evolution of new applications with stringent requirements, ensuring high-performing WANs is a critical priority for businesses looking to remain competitive and provide a seamless customer experience. This paper explores using Rein-forcement Learning (RL) algorithms on Software-Defined Wide Area Network (SD-WAN) to improve QoS and reduce costs. SD-WAN allows for the dynamic reconfiguration of network devices in real-time, better meeting network measurements and service requirements. By leveraging self-learning techniques such as RL, which exploits feedback mechanisms, we can improve network availability by automatically routing traffic over existing network technologies. We also compare two SD-WAN topology scenarios, including direct WAN connections between Customer Premises Equipment (CPEs) within enterprise premises and CPEs used as peering points for traffic routing. Our approach shows promising results regarding network performance and cost-effectiveness, which can benefit businesses looking to improve their network infrastructure.
A Comparative Study of Sentiment Analysis on Public Opinion Data Using BERT and LSTM Architecture Based on a Transformers Approach

Human being are able to express their thoughts, perspectives, and opinions on a variety of topics through online forums, blogs, social media sites, digital discussion forums, as well as other virtual spaces as a result of the lightning-fast growth of technological advancements like the Internet and the rise of social networks. These viewpoints may help businesses like hotels and the tourism industry in enhancing their commodities and amenities for the visitors they serve. People are able to learn considerable information from the analysis of public sentiment, thoughts, perceptions, opinions, and so on. Sentiment analysis is essential for exploring the public’s sentiment or opinion because the findings may provide us with insightful information. By using Deep Learning technology to analyze the emotional tenor of comments, it is now possible to quickly understand public opinion on hotel product preferences, advertising tactics, and financial goals. Sentiment Analysis is another Natural Language Processing (NLP) application that has encountered an immense surge in popularity over the past few years. Nevertheless, the difficulties in NLP are impeding sentiment analysis’s efficacy and accuracy. Deep learning models have been highlighted to be a promising response to NLP’s issues lately. This research compares the BERT model with the LSTM model to accomplish sentiment analysis objectives to overcome this limitation. We developed a customized unique data set based on the general consensus regarding hotel reviews to properly implement the aforementioned method. Last but not least, a comparative study between the BERT and LSTM models has been conducted on the experimental results for the input features.
Automated Image Caption Generation using Deep Learning

Image captioning means generating relevant texts from images that describe the image. With the growing advancement of deep learning, automatic image caption generation has become an exciting problem among researchers. Image captioning is used in various fields of computer science including computer vision and NLP. It is helpful in many cases such as visual impairment. Though many research works have already been published on this topic using different deep learning models, we can work with various features of the deep learning models to achieve better accuracy. In this paper, we are focusing on combining several deep-learning models to get the desired accuracy. VGG16 model is used which is a CNN architecture to extract essential features from the image. For the generation of relevant captions, LSTM is adopted rather than RNNs as LSTM generates better captions compared to RNNs and it consumes less time. Finally, the model is trained on Flickr 8k datasets.
Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.
Enhancing Epileptic Seizure Detection Through Advanced Artificial Intelligence Analysis of EEG Signals

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Seizures are episodes of disturbed brain function caused by brief hypersynchronous neuronal firing. EEG is the quintessential modality for monitoring and identifying these discordant patterns. This study aimed to enhance the diagnostic accuracy of detecting epileptic seizures by applying sophisticated analytical techniques, including machine learning and deep learning, to electroencephalogram (EEG) data. we performed comprehensive preprocessing on a standardized dataset of EEG records from 100 subjects, with 50 diagnosed with epilepsy and 50 classified as non-epileptic controls. The preprocessing encompassed steps including data formatting, labeling, scaling, cleaning, and partitioning into training and test sets. The machine learning models employed were Random Forest Classifier (RFC) and Gradient Boosting (GB), while Long Short-Term Memory (LSTM) networks represented deep learning. Rigorous performance assessment revealed remarkable effectiveness for all three models in classifying EEG data into seizure and non-seizure categories. Specifically, the RFC achieved an accuracy of 99.7%, GB demonstrated 92% accuracy, and the LSTM network exhibited 96.9% accuracy. The robust performance across these computational methods highlights their potential to significantly improve seizure detection and diagnosis in epilepsy care through the analysis of EEG signals.
UDnet: A Polyp Image Segmentation Method Based on Derivative Network

A polyp image segmentation algorithm based on derivative networks is proposed because of some problems such as virtualization, holes, rough segmentation, and imperfect edge generation of traditional UNet. An improved lightweight U-shaped network structure is utilized to achieve rough segmentation of boundary maps and polyp feature extraction. Then a derivative network is built, which can dynamically adjust the segmentation focus based on the input image to model the probability of each pixel being positioned in the boundary region. Combining the contribution weights of various features in polyp images, an adaptive threshold scoring mode is adopted to further determine the boundary region. A feature aware cross attention module is added to enhance the feature comparison between polyps and healthy intestinal walls. The experimental results indicate that UDnet proposed achieves better segmentation accuracy, parameter quantity, and polyp area generation compared with U-Net, ResUnet and other networks.