Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design and Performance Evaluation of Software-defined Solar PV Systems

To obtain the maximum solar radiation from sunrise to sunset, a robust and dynamic tracking system is required for solar photovoltaic (PV) panels. However, existing solar tracking systems encounter several issues, such as additional cost and power consumption, complexity in installation and integration, susceptibility to environmental conditions, tracking accuracy, compatibility, reliability, and scalability problems. To address these challenges and to provide an efficient, reliable, and cost-effective solution, a software-defined networking (SDN)-assisted tracking system can be deployed. Therefore, this paper proposes an SDN-enabled solar tracking system that can provide a more scalable, secure, and adaptive tracking platform for solar PV panels. In addition, this proposed framework allows centralized control and real-time monitoring, and virtualized architecture rather than a conventional hardware-based tracker. Besides, a robust panel adjustment algorithm is also developed and MATLAB’s Simulink module is utilized to demonstrate its effectiveness based on I-V and P-V characteristics. Interestingly, simulation results show that our proposed framework has outperformed the traditional tracking system and offers 22% improved energy generation as well as provides a software-based real-time tracking platform.
PikFresh - Fruit Quality Detection using ResNet50

Fruit quality detection has started becoming an increasing concern for inexperienced individuals. Upcoming generations struggle the most when it comes to buying fruit in the market of idle quality, as they lack the experience required to purchase something that is fresh. This novel system aims to ease the process of analysing the quality of fruit. This paper focuses on the transfer learning algorithm ResNet50, integrated with the React Native framework, to build a complete end-to-end solution. A customised dataset was created for certain fruits. The user will be given the option to scan a fruit, taking a live video capture from all directions, on the basis of which a report will be generated. During the process, the user is directed towards a questionnaire pertaining to a specific fruit, which helps in assessing the quality on the basis of sensory perception. The report obtained consists of various details, such as recommendations to buy, the shelf life of the fruit, whether it is adulterated, and whether it should be refrigerated.
Synchronization likelihood based functional connectivity investigations on participants with autism spectrum disorder

The term autism spectrum disorder is frequently used to describe a wide range of conditions that affect how the brain develops. Electroencephalography is now the procedure that is most frequently used to look for biomarkers of autism (EEG). EEG recordings often offer a vast quantity of data with dynamic properties. In this study, alpha waves in the frontal lobes of relaxed healthy, and autistic participants are investigated. The raw EEG data is first pre-processed utilizing several techniques, such as normalization, filtering, and re-referencing. The disturbed connectivity theory has drawn a lot of research as a probable pathophysiological underpinning for autism spectrum disorders (ASD). According to this hypothesis, difficulties with the approach the brain synchronizes and organizes activity across multiple regions may account for the clinical symptoms of ASD. To understand the neural synchronization using the parameter synchronization likelihood, this study employed electroencephalography recorded from participants when they were relaxed and performed a functional connectivity-based analysis on the preprocessed EEG signals. Using a dataset supported by the King Abdulaziz Hospital in Jeddah, Saudi Arabia, the suggested technique produced encouraging results in testing.
Integrating gender into World Bank financed transport programmes - case study: South Africa Shova Kalula

The purpose of this project is to implement a national transport partnership to promote the use of bicycles and work cycles as means of transport. The partnership was facilitated by the National Department of Transport and targeted partners such as local government, provincial government, business, non-profit organizations and foreign governments and organizations. The overall aim was to implement one million bicycle transport packages in South Africa over ten years. A bicycle transport package will include most of the following: a low cost new or used bicycle; a training course in riding and maintenance and transport uses; access to service and support from a local/regional micro business; special attention to empowering female users; an Edu-bike Africa learner's workbook (where relevant); the option of earning a bike through working in the micro business; an infrastructure review of the area's potential for safe bicycle transport; periodic visits by a mobile bicycle transport clinic.
Forecasting in stock market with Machine Learning: A State of Art

Machine learning has become increasingly crucial in stock market forecasting due to its capacity to analyze large volumes of data, detect patterns, and make predictions based on historical trends. It offers several benefits, including handling complex datasets, recognizing and predicting patterns, adaptability and scalability, automating trading strategies, risk management and portfolio optimization, and more. However, it’s important to recognize that stock markets are influenced by numerous unpredictable factors, and there are inherent limitations and uncertainties in predicting market movements. Therefore, machine learning should be utilized as one tool among many in the investor’s decision-making process, complementing human judgment and domain expertise. This survey article explores the use of machine learning techniques in stock market price prediction, examining different models and their success rates across various countries and datasets. It also delves into the commonly used data sources for stock price prediction and discusses methods for processing this data. Through this study, it becomes evident how machine learning techniques have the potential to enhance stock price prediction and contribute to a deeper understanding of financial markets.
Obfuscated Malware detection using Machine Learning models

The number of malware attacks has been growing at an alarming rate especially in the recent years. Cyber criminals equip themselves with the latest technologies and discover new methods of attack every day. Therefore, researchers have paid a lot of attention to malware detection in order to lower malware threats. Static analysis or behaviour analysis are used in majority of the investigative experiments. New studies indicate that modern malware files employ diverse techniques to evade detection and analysis, rendering both types of analyses susceptible. This renders the extracted features potentially useless and only serves as a diversion for malware analysts. However, volatile memory holds significant potential for extracting valuable data and insights regarding the characteristics and behaviors of malware. Furthermore, memory analysis possesses the capability to detect atypical forms of malware, such as malware residing in computer memory (in-memory) and malware that operates without leaving a trace on the disk (fileless). The study aims to detect malware attacks with high accuracy by employing feature engineering followed by usage of machine learning methods. In this present paper we examine memory data from the CIC-Malmem-2022 dataset containing threat analysis data, the algorithms used for malware detection along with their performance metrics and compare the various Machine Learning models and their use.
Bivariate polynomial key update based on interpolation for WSN

In bivariate polynomial based key management schemes polynomial is fixed and it is secure upto t nodes. In polynomial key management schemes no polynomial update and key update.If no polynomial update and key update if t nodes are captured entire network will compromise. This problem addressed in this paper. The proposed approach uses t degree bivariate polynomial that is generated uniquely by each cluster head from their predeployed points. In each session, the base station sends a new polynomial associated point to the cluster head. After$\left({_2^{t + 2}}\right)$ sessions, the pairwise key is dynamically updated between pairs to avoid a node capture attack. Also, in each session, cluster heads generate a new random number to avoid a replay attack. Mutual authentication to avoid man in the middle attack. In results it takes less memory . Security analysis is verified in Scyther Tool.
Reenvisioning velocity reversal as a diversity of hydraulic patch behaviours

Past research investigated the surpassing of mean velocity at riffle cross sections by that at pool cross sections for flows up to bankfull, termed ‘velocity reversals’, to understand one mechanism by which riffle–pool relief is maintained. This study reenvisioned the classic velocity reversal by documenting stage‐dependent changes to the locations of peak velocity without cross sections. Instead, the dynamics of peak velocity patches were considered for flows spanning 0.2 to 22 times bankfull discharge through the use of a high‐resolution DEM and two‐dimensional hydrodynamic modelling. A remarkable diversity in peak velocity patch behaviour was found across discharges, including gradual expansion and shifting as well as abrupt disappearance and emergence relative to the low‐flow patch locations. These behaviours blended together to varying degrees to produce many reversals in peak velocity across morphological units, but it took substantially higher than bankfull discharge for peak velocities to move from riffles and chutes to fast glides and pools. The discharges at which reversals occurred among morphological units were significantly higher for the valley‐confined reach than for the anastomosing reach. Copyright © 2016 John Wiley & Sons, Ltd.
Medical Bio-Modeling: A First Report on the Implementation of 3D Printing for Congenital Anomalies in Peru.

Congenital anomalies are among the leading causes of mortality worldwide, predominantly affecting lower-middle-income countries. Furthermore, these complex and rare anatomical structures are sometimes not fully comprehensible until the surgical procedure begins. To address these challenges, increased innovation and new procedures are needed, as multiple factors hinder the advancement of neonatal surgery in our country. Obtaining clinical models represented in three dimensions (3D) can provide additional details and hands-on surgical training for medical doctors, thereby reducing the risks associated with these surgeries. Clinical models have already demonstrated success as a potential supplementary technique for medical imaging. However, the existing infrastructure and local resources present significant challenges. Therefore, an in-house 3D printing fabrication procedure must be made available. In this retrospective study, we are combining image computing software and 3D printing technology to create, for the first time, five biomodels of patients with congenital anomalies in a real clinical setting in a middle-income country like Peru. The evidence obtained will contribute to the incorporation of 3D printing in the field of diagnosis and treatment at one of Peru's most critical public children's hospitals. This breakthrough opens the door for future developments and the integration of these facilities into more public institutions.
Cloud Based Multi Factor Crop Motoring and Irigation Optimization

We all know that plantations are one of the biggest sectors in the contemporary world. 30worldwide is accounted for by it. India is a major agricultural country, and the safety of the nation’s food supply is significantly influenced by agricultural productivity. In comparison to other wealthy countries, India has significantly less farmland per person than the world average, as well as lower production values per person and lower land yields per unit. As a result, we must find ways to enhance output using the limited natural resources that are presently available if we are to meet the difficulties of food production.
Accuracy Comparison of T3ster-Master and Optimization-based Network Identification

For a thermal analyst, the evaluation of structure functions is as a powerful technique to optimize heat transfer processes and to study thermal phenomena. The Optimization-based Network Identification (ONI) offers an alternative approach to construct structure functions, complementing existing methods for thermal transient analysis. The accuracy of ONI in modeling thermal transients is investigated in this study. The method is rigorously compared with a self-implemented variant of Network Identification by Deconvolution (NID) and the widely used implementation of NID in the Siemens Simcenter T3ster Master. While ONI encounters challenges in heat paths with pronounced heat spreading effects, its implicit modeling of structure functions aids in determining partial thermal resistances. In conclusion, ONI’s precision and convergence quality offer a promising way to comprehend thermal dynamics in semiconductor devices.
Optimizing LightGBM for Intrusion Detection Systems using GOA

Increasing popularity of IoT devices across the world is because of its applicability in wide range of areas and disciplines like healthcare, education, business, defense etc. The information is continuously being shared as they work 24x7, thus making vulnerable to several types of attacks. Intrusion Detection Systems are smart intelligent systems that detects the occurrence of any malicious activity. Due to the growing size of the internet, intruders are trying new ways to inject newer kinds of attacks and perform malicious activities. Hence, there is a need of robust and reliable IDSs. This paper presents a methodology using LightGBM classifier with hyper parameter tuned using Grasshopper Optimization Algorithm using NetFlow based UNSW-NB15 dataset. The proposed methodology aims to give a contribution to several works done using NetFlow based data, achieving ∼97% of the detection rate.
Physics-Informed Neural Networks for Monitoring Dynamic Systems: Wind Turbine Study Case

This work addresses the use of Physics-Informed Neural Ordinary Differential Equations (PINODEs) for the identification and monitoring of dynamic systems. By combining prior knowledge regarding the physics that governs the behavior of a dynamic system with the flexibility and learning capabilities of Artificial Neural Networks (ANNs), it is possible employ data-driven methods that result in a robust representation of the systems, even without full knowledge of the underlying physical phenomena, while retaining a degree of interpretability of the model’s outputs. A description of the overall framework for modeling the system identification problem and training the ANNs under the is presented, along with an application case study for condition monitoring of a wind turbine’s gearbox using vibration data. The results demonstrate the ability of the identified model to generalize with great accuracy to scenarios not accounted for during training, a property attributed to the inclusion of information regarding the physics of the problem during the training procedure. It is also shown that when exposed to data collected during a fault condition, the model’s output significantly deviate from the actual measurements, hence its potential use as a tool for condition monitoring and fault detection is also successfully demonstrated.
Prioritized and optimum Resource Allocation for Public Safety Applications in Overlay D2D Communication

D2D communication can be used for public safety as well as commercial applications. Public safety applications require efficient delivery of crucial information to concerned authorities for sharing information and coordination to facilitate effective incident response. Commercial applications include messaging, streaming gaming, Etc. The emergence of 5G cellular networks has introduced a wide range of connected devices, resulting in increased challenges in spectrum reuse due to a more significant number of devices competing for limited resources. There is no distinction between public safety and commercial applications for resource allocation, and all user equipment in the network is treated equally. However, public safety communication has to get more privileges than commercial applications for resource allocation. In this study, we propose Weighted Round Robin(WRR) algorithm for resource allocation to D2D users in the overlay mode D2D communication cellular network. In the standard round-robin algorithm, all users are treated equally well irrespective of the application types and allocate resources sequentially one after another in a round-robin manner. In a WRR algorithm, we prioritize public safety applications over commercial applications for resource allocation in an inband-overlay mode and maximize the number of D2D pairs participating in the public safety application. The simulation result shows that the proposed algorithm prioritizes public safety applications for different weights.
Detection of Pneumonia in Chest X-Ray Using Ensemble Learners and Transfer Learning with Deep Learning Models

A commonly implemented imaging method used for detecting/diagnosing pneumonia is chest X-ray images, but interpreting these images can be extremely complicated requiring advanced skill sets. In this document, we introduce an innovative approach using ensemble learning which combines several pre-trained Deep Learning models' strengths with advanced transfer learning techniques aimed at enhancing lung infection detection through deep-learning predictive capacity utilization. We evaluated our methodology using publicly available datasets containing numerous X-ray scans resulting in optimally accurate predictions that could have substantial implications concerning medical practice improvement efficiency-wise when diagnosing patients exhibiting symptoms matching those indicative of pneumonic infections, thus allowing us to proceed forward successfully implementing more streamlined protocols going onward into future clinical settings.
A Soil pH Sensor and a Based on Time-Series Prediction IoT System for Agriculture

Agriculture 4.0, which combines precise sensors, The Internet of Things (IoT) and Artificial Intelligence (AI) revolutionize precision agriculture. This paper addresses the union of technological solutions from new sensors, AI, IoT, and Edge Computing to deliver an innovative solution. The pH is one of the most critical parameters of the soil, presenting a diagnosis of the soil. Depending on the pH value, plantations can develop or not. In this work, an accurate and robust soil pH sensor was developed, as well as a deep learning model for time series prediction operating at the edge of the IoT system. The proposed sensor showed good performance and a low error percentage compared to commercial sensors and pH measurement by traditional laboratory methods. To take advantage of the richness of data generated by the pH sensor, a neural network LSTM(long-term memory architecture) was implemented for time series prediction. The LSTM model integrated into the edge computing system demonstrated accuracy in periodic pH predictions. This implementation provides valuable insights for agriculture and smart practices, empowering farmers and agronomists to maximize crop productivity.
Novel hybrid classifier synergizing 3D_2D CNN with Support Vector Machine to enhance Hyperspectral image classification

Hyperspectral imaging (HSI) is a potent tool that offers accurate identification and analysis of materials and objects in various domains ranging from remote sensing, agriculture, chemo-metric analysis for agricultural products and bio-medical applications, and much more. However, accurately categorizing HSI images remains challenging due to high dimensionality pertaining to spectral resolution in multiple bands, inadequate training samples, and pixel mixing. Deep learning Convolutional Neural Networks (CNN) have undergone development over time to process data of varied dimensions as 1D CNNs were developed to handle time series data, while 2D CNNs operate on spatial image data, and 3D CNNs evolved for processing 3D volumetric data. This paper proposes a synergistic hybrid classifier model that generalizes the 3D_2D CNN deep learning model to extract features from HSI images automatically and a robust Support Vector Machine (SVM) classifier to classify them accurately with less computational efficiency. The prominent intention of this proposed classifier is to be robust in terms of efficiency and less computationally complex. The proposed method involves HSI image dimensionality reduction using principal component analysis (PCA) and 3D_2D convolutional layers to extract significant spectral-spatial features. These feature maps are flattened, which is then used to train the SVM classifier. On the Kennedy Space Centre (KSC) HSI dataset, experimental results demonstrate that the proposed hybrid technique provides excellent accuracy and outperforms the modern techniques regarding classification precision and computational effectiveness. The proposed hybrid 3D 2D CNN synergized with the SVM approach achieved 99.8% accuracy, which is a 1.31% improvement compared to the existing Cube CNN with an SVM similar kind of hybrid classifier.
Solution of optimal power flow problem for hybrid power system using a novel laplacian mutual learning assisted backtracking search algorithm

The researcher uses the optimal power flow (OPF) problem primarily for the economical operation of the power system while keeping different operational aspects within safe operating limits. Due to the depletion of fossil fuels and the increasing impact of environmental pollution, renewable energy resources (RERs) have been incorporated with thermal generation systems for the past few decades. The OPF problem gets more complex when applied to a power system integrated with RERs with significant intermittencies. In the current paper, an OPF problem is used for the power production cost minimization of a hybrid power system, including thermal generator (TG), wind power (WP), photovoltaic power (PVP), and tidal power (TDP). We propose a Laplacian mutation operation with a mutual learning scheme to enhance the backtracking search algorithm's exploration, exploitation, and convergence profile. The proposed technique is called Laplacian mutual learning-assisted BSA (LMLBSA). The proposed LMLBSA is applied to minimize the power production cost of the hybrid power system. To authenticate the superiority of the proposed technique, the results are compared to BSA with specular reflection learning (BSA_SRL), orthodox BSA and two robust existing techniques : grey wolf optimizer (GWO) and particle swarm optimization (PSO). The results indicated the proposed LMLBSA outperforms the competitors in terms of highly accurate and quality solutions.
Harnessing TernausNet For The Segmentation Of Heart From Chest X-ray For The Recognition Of Atrial Septal Defect

Semantic segmentation of chest X-rays can aid in the early detection of cardiac conditions, including atrial septal defect, which improves patient outcomes. The U-Net architecture is a popular approach to semantic segmentation, but it has some limitations such as the risk of vanishing gradients and the difficulty of capturing global context information. The TernausNet architecture is an improvement over U-Net that addresses these limitations by using residual connections, VGG16 encoders, stretching convolutions, and batch normalization. In this paper, we compare the performance of TernausNet and U-Net on the cardiomegaly datasets showing that TernausNet achieves better segmentation accuracy. We also analyze the advantages of TernausNet over U-Net in terms of architectural design and explain how these advantages improve the performance of the model.