IECE Transactions on Intelligent Systematics

Free Access | Research Article | 12 December 2024

Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 203-214, 2024 | DOI:10.62762/TIS.2024.461943

Abstract

The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 93% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake... More >

Graphical Abstract

Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework

Free Access | Research Article | 12 November 2024

Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 190-202, 2024 | DOI:10.62762/TIS.2024.751418

Abstract

The challenge of accurately estimating effort for software development projects is critical for project managers (PM) and researchers. A common issue they encounter is missing data values in datasets, which complicates effort estimation (EE). While several models have been introduced to address this issue, none have proven entirely effective. The Analogy-Based Effort Estimation (ABEE) model is the most widely used approach, relying on historical data for estimation. However, the common practice of deleting cases or cells with missing observations results in a reduction of statistical power and negatively impacts the performance of ABEE, leading to inefficiencies and biases. This study employ... More >

Graphical Abstract

Improving Effort Estimation Accuracy in Software Development Projects Using Multiple Imputation Techniques for Missing Data Handling

Free Access | Review Article | 09 November 2024

Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 176-189, 2024 | DOI:10.62762/TIS.2024.818917

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This systematic review and meta-analysis examine the transformative impact of artificial intelligence (AI) applications on forensic odontology, specifically focusing on the enhancement of identification accuracy and operational efficiency. Traditionally, forensic odontology depends on detailed dental records for human identification purposes. However, with the integration of AI-driven advancements, including machine learning algorithms and image recognition systems, the field is undergoing significant evolution. These AI technologies offer notable improvements in the precision of complex tasks such as bite mark analysis, dental age estimation, and dental record matching, while simultaneously... More >

Graphical Abstract

Comprehensive Evaluation of Artificial Intelligence Applications in Forensic Odontology: A Systematic Review and Meta-Analysis

Free Access | Research Article | 09 November 2024

In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 161-175, 2024 | DOI:10.62762/TIS.2024.585616

Abstract

Sentiment analysis is the process of identifying and categorizing opinions expressed in a piece of text. It has been extensively studied for languages like English and Chinese but still needs to be explored for languages such as Urdu and Hindi. This paper presents an in-depth analysis of Urdu text using state-of-the-art supervised learning techniques and a transformer-based technique. We manually annotated and preprocessed the dataset from various Urdu blog websites to categorize the sentiments into positive, neutral, and negative classes. We utilize five machine learning classifiers: Support Vector Machine (SVM), K-nearest neighbor (KNN), Naive Bayes, Multinomial Logistic Regression (MLR),... More >

Graphical Abstract

In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches

Free Access | Research Article | 29 October 2024

Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 145-160, 2024 | DOI:10.62762/TIS.2024.640345

Abstract

Cataracts are a leading cause of blindness in Pakistan, contributing to more than 54% of cases due to poor living condition, nutritional deficiencies, and limited healthcare access. Early detection is critical to avoid invasive treatments,but current diagnostic approaches often identify cataracts at advanced stages. This paper presents an advanced,automated cataract detection system using deep learning specifically the ResNet-50 architecture, to address this gap. The model processes fundus retinal images curated from diverse datasets, classified by ophthalmologic experts through a rigorous three-stage process. By leveraging the ResNet-50 model, cataracts are categorized into normal,moderate,... More >

Graphical Abstract

Enhancing Ocular Health Precision: Cataract Detection Using Fundus Images and ResNet-50

Free Access | Review Article | 21 October 2024

Transforming Industry 4.0 Security: Analysis of ABE and ABA Technologies

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Jibran Saleem

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Umar Raza

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William Holderbaum

IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 127-144, 2024 | DOI:10.62762/TIS.2024.993235

Abstract

The sharing of data and private information has been greatly improved by Industry 4.0's broad usage of cloud technologies. In their quest to improve their services, many firms have made automation and effective authentication a priority. As a result, in Industry 4.0, Attribute-Based Encryption (ABE) and Attribute-Based Authentication (ABA) have established themselves as dependable models for data sharing across cloud environments. For difficult situations like fine-grained access control and secure authentication, these models offer practical answers. Organizations can utilize ABA to specifically authenticate people based on their attributes, ensuring appropriate and safe access to critical... More >

Graphical Abstract

Transforming Industry 4.0 Security: Analysis of ABE and ABA Technologies

Free Access | Research Article | 20 October 2024

Comparison of Deep Learning Algorithms for Retail Sales Forecasting

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Rana Shakeel Ahmed

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Muhammad Hasnain

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Muhammad Hamza Mahmood

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Muhammad Abid Mehmood

IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 112-126, 2024 | DOI:10.62762/TIS.2024.300700

Abstract

We investigate the use of deep learning models for retail sales predictions in this research. Having a proper sales forecasting can lead to optimization in inventory management, marketing strategies, and other core business operations. This research proposed to assess deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron and hybrid CNN-LSTM model. The models are further improved by using some dense layers to embed daily sales data from the biggest pharmaceutical firm in the study. Models are then trained on 80% of the dataset and tested on remaining 20%. The accuracy of the proposed research is compared using evaluation metrics... More >

Graphical Abstract

Comparison of Deep Learning Algorithms for Retail Sales Forecasting

Free Access | Research Article | 29 September 2024

Investigation on the Mechanism of Nebulized Droplet Particle Size Impact in Precision Plant Protection

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 102-111, 2024 | DOI:10.62762/TIS.2024.307219

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Precision plant protection, a crucial facet of precision agriculture, assumes a paramount role throughout diverse stages of agricultural pesticide utilization. It not only furnishes indispensable reference parameters for agricultural production but also minimizes the employment of pesticides and their environmental footprint. This investigation employs a laser particle size analyzer to gauge the particle size information of the atomization field under assorted conditions, commencing with ground plant protection. The findings reveal that particle size escalates with the ascent of spray pressure and spray angle while diminishing with their augmentation. It proposes that pressure adjustments ca... More >

Graphical Abstract

Investigation on the Mechanism of Nebulized Droplet Particle Size Impact in Precision Plant Protection

Free Access | Research Article | 29 September 2024

On-line Configuration Identification and Control of Modular Reconfigurable Flight Array

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 91-101, 2024 | DOI:10.62762/TIS.2024.681878

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With the increasing complexity of the working environment and the diversification of mission requirements of UAVs, traditional UAVs have a fixed structure and single function. It is difficult to be applied in occasions with complex environments and changing load demands. The modular reconfigurable flight array (MRFA) is composed of no less than four isomorphic unit modules that are freely spliced together. By adding or removing flight unit modules and adjusting the arrangement of flight unit modules, the configuration of the MRFA can be changed, so that it can adapt to complex environments and then complete different flight missions. In the process of MRFA research and development, online co... More >

Graphical Abstract

On-line Configuration Identification and Control of Modular Reconfigurable Flight Array

Free Access | Research Article | 27 September 2024

Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities

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Yuqi Lin

IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 79-90, 2024 | DOI:10.62762/TIS.2024.952592

Abstract

Accurate predictions of traffic flow are very meaningful to city managers. With such information, traffic systems can better coordinate traffic signals and reduce congestion. By understanding traffic patterns, navigation systems can provide real-time routing suggestions that avoid traffic jams, save time, and reduce fuel consumption. However, traffic flow will be interfered with by multiple factors such as collection time and place. In this paper, we propose to use stochastic configuration networks (SCNs) to predict the traffic flow. The network is trained through stepwise construction, and the network parameters are effectively optimized based on the approximation theorem and convergence an... More >

Graphical Abstract

Long-term Traffic Flow Prediction using Stochastic Configuration Networks for Smart Cities

Free Access | Research Article | 23 September 2024

Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations

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Ghulam E Mustafa Abro

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Zain Anwar Ali

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Ayman M Abdallah

IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 69-78, 2024 | DOI:10.62762/TIS.2024.807714

Abstract

A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulat... More >

Graphical Abstract

Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations

Free Access | Review Article | 23 September 2024

Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application

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Wei Zhang

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Qian Hong

IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 58-68, 2024 | DOI:10.62762/TIS.2024.680959

Abstract

The integration of graph neural networks (GNNs) with brain functional network analysis is an emerging field that combines neuroscience and machine learning to enhance our understanding of complex brain dynamics. We first briefly introduce the fundamentals of brain functional networks, followed by an overview of Graph Neural Network principles and architectures. The review then focuses on the applications of these networks and address current challenges in the field, such as the need for interpretable models and effective integration of multi-modal neuroimaging data. We also highlight the potential of GNNs in clinical perimenopausal areas such as perimenopausal depression research, demonstrat... More >

Graphical Abstract

Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application

Free Access | Research Article | 20 September 2024 | Cited: 1

A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 49-57, 2024 | DOI:10.62762/TIS.2024.329126

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This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute CO2 gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages se... More >

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A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

Free Access | Research Article | 29 May 2024 | Cited: 6

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 40-48, 2024 | DOI:10.62762/TIS.2024.137329

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Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >

Graphical Abstract

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

Free Access | Research Article | 27 May 2024 | Cited: 5

YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 30-39, 2024 | DOI:10.62762/TIS.2024.137321

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In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >

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YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image

Free Access | Research Article | Feature Paper | 26 May 2024

Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU

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Yeqing Wang

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Binbin Wang

IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 19-29, 2024 | DOI:10.62762/TIS.2024.136995

Abstract

A pedestrian navigation system (PNS) that only relies on the foot-mounted IMU is useful for various applications, especially under some severe conditions, such as tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments such as fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an improved zero velocity detection algorithm to improve the accuracy of gait detection. Then, combine the Kalman filter with the zero velocity update algorithm to recognize gait accurately and ta... More >

Graphical Abstract

Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU

Free Access | Research Article | 25 May 2024 | Cited: 3

Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data

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Hanchi Ren

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Yeqing Wang

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Huijun Ma

IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 10-18, 2024 | DOI:10.62762/TIS.2024.136898

Abstract

To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >

Graphical Abstract

Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data

Free Access | Research Article | 15 May 2024 | Cited: 2

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 3-9, 2024 | DOI:10.62762/TIS.2024.136895

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Frontend feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational costs and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tom... More >

Graphical Abstract

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

Open Access | Editorial | 17 April 2024

Editorial: Intelligent Systematics: A New Transactions

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Xuebo Jin

IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 1-2, 2024 | DOI:10.62762/TIS.2024.100001

Abstract

Presents information on the new IECE Transactions on Intelligent Systematics. More >

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