Efficient Detection System Research Articles

An approach towards the detection of eye cancer using configured convolution neural network with sine cosine fitness gray wolf optimizer

PURPOSE: Increasing technological advancements in processing and storage have made it easier to handle formerly difficult jobs like disease diagnosis or semantic segmentation. Eye cancer is a rare but deadly disorder that, if misdiagnosed, can cause blindness or even death. It is essential to find eye cancer early in order to successfully treat it and enhance patient outcomes. The usage of DL methods for medical image analysis, particularly the identification of eye cancer, has fascinated increasing consideration in current era. The demand for efficient tool to detect the eye cancer emphasize the need for reliable detection systems. Examining how explainable deep learning techniques, in which the model’s decision-making process can be understood and visualized, can increase confidence in and adoption of the deep learning-based approach for detecting eye cancer. Expert input is necessary to train machine learning algorithms properly. As it necessitates knowledge of ophthalmology, radiography, and pathology, this can be difficult for eye cancer identification. The main purpose of the study is to detect the eye cancer with at most accuracy with the utilization of Deep learning-based approach. METHODS: There are four steps involved to achieve the efficient detection system. They are pre-processing, segmentation, augmentation, feature extraction with classification. The Circle Hough Transform is applied to detect the edges in the image. The dataset size is increased by shifting, rotating and flipping augmentation techniques. Deep learning-based approach is suggested for the automatic detection of eye cancer. The two methods named 3XConPool and 10XCon5XPool were investigated using Python learning environment. The two techniques 3XConPool and 10XCon5XPool imply on the Sine Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm for the adjustment of the hyperparameters. The 3XConPool and 10XCon5XPool methods with SCFGWO are compared with each other and also with the other existing methods. RESULTS: As a comparison to the earlier techniques, the suggested configured Convolution Neural Network with SCFGWP exceeds with regard to high accuracy, recall and precision. The suggested 10XCon5XPool with SCFGWO obtains 98.01 as accuracy compared to other method 3XConPool which results 97.23% accuracy. CONCLUSION: The Proposed Method 1 and Proposed Method 2 is presented here, where Proposed Method 2 with 5 times convolution layer with pooling layer yields high accuracy compared to proposed method 1. The main contribution by the SCFGWO algorithm resulted in the achievement of accuracy. This study will open the door for further investigation and the creation of deep learning-based techniques with optimization for ophthalmic processing.

Effective Intrusion Detection System Using Deep Learning for DDoS Attacks

An increasing demand for information technology and cloud computing has led to various security threats over internet. Amongst all types of threats, Distributed Denial of Service (DDoS) attacks are the most common, posing a challenge to a cyber security. It allows unauthorized users to gain access to services, and preventing authorized users. Traditional intrusion detection, antivirus software, and firewalls are not enough to detect these attacks. This paper proposed an efficient deep learning-based intrusion detection system (IDS) for DDoS attacks detection. Proposed model is a hybrid CNN-LSTM model which is combination of best DL algorithms to take advantage of CNN to extract spatial features and LSTM to extract temporal features. The real-time DDoS-specific benchmark-dataset CICDDoS2019 used for training, testing, and validation. An autoencoder has been used for dimensionality reduction, and SMOTE method is used to balance the minority class of dataset. The experimental results proved that the model outperforms than other state-of-the-art models, with an accuracy score of 99.86%.

Spam analysis and classification of the dynamic message using a vectorizing technique with multi-model machine learning algorithm

Spam analysis and classification of dynamic messages is an essential task in order to combat the ever-increasing volume of unsolicited and malicious emails. One effective approach is to employ a vectorizing technique along with a multi-model machine learning algorithm. This approach involves representing email messages as high-dimensional vectors, capturing various features such as word frequencies, presence of specific keywords, and structural characteristics. By transforming the text into numerical representations, the machine learning algorithm can then learn patterns and make predictions based on these representations. The use of a multi- model algorithm allows for the integration of different classification models, each with its own strengths and weaknesses, to enhance the overall performance. This approach can achieve high accuracy by leveraging diverse learning methods and combining their predictions. Furthermore, the approach is dynamic in nature, meaning that it can adapt to new forms of spam and evolving attack strategies. The key challenge lies in selecting appropriate features and tuning the parameters of the algorithm to ensure optimal performance. The results of this study can contribute to the development of more effective and efficient spam detection systems, helping users to filter out unwanted and potentially harmful messages. Keywords: spam analysis, dynamic messages, classification, vectorizing techniques, multi-model machine learning algorithm, email filtering, unsolicited emails, malicious emails, feature extraction, pattern recognition.

High-performance eight-channel system with fractal superconducting nanowire single-photon detectors

Superconducting nanowire single-photon detectors (SNSPDs) have become a mainstream photon-counting technology that has been used in various applications. So far, most multi-channel SNSPD systems, either reported in literature or been commercially available, are polarization sensitive, that is, the system detection efficiency (SDE) of each channel is dependent on the state of polarization of the to-be-detected photons. Here, we report on an eight-channel system with fractal superconducting nanowire single-photon detectors working in the wavelength range of 930-940 nm that all feature low polarization sensitivity. In a close-cycled Gifford-McMahon cryocooler system with the base temperature of 2.2 K, we install and compare the performance of two types of devices: (1) SNSPD, composed of a single, continuous nanowire, and (2) superconducting nanowire avalanche photodetector (SNAP), composed of 16 cascaded units of two nanowires electrically connected in parallel. The highest system detection efficiency (SDE) among the eight channels reaches 96−5+4%, with polarization sensitivity of 1.02 and dark-count rate of 13 counts per second. The average SDE for eight channels for all states of polarization is estimated to be 90 ± 5%. We conclude that both the SNSPDs and the SNAPs can reach saturated, high SDE at the wavelength of interest, and the SNSPDs show lower dark- (false-) count rates while the SNAPs show better properties in the time domain. Using this system, we showcase the measurements of the second-order photon-correlation functions of light emission from a single-photon source based on a semiconductor quantum dot and from a pulsed laser. We believe that our work provides new choices of systems with single-photon detectors combining the merits of high SDE, low polarization sensitivity, and low noise that can be tailored for different applications.

Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques.

Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign.

This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.

Disorder enhanced relative intrinsic detection efficiency in NbTiN superconducting nanowire single photon detectors at high temperature

The intrinsic detection performance of superconducting nanowire single photon detectors (SNSPDs) is highly dependent on the superconducting properties of underlying thin films. This report outlines the enhancement of detection performance for single telecom wavelength photons in disordered NbTiN SNSPD at 4.2 K. By increasing the nitrogen content and deposition pressure, the NbTiN films show suppression in critical temperature and an increase in sheet resistance. Notably, the resulting SNSPDs display a broader saturation plateau at 2.2 K, leading to superior detection performance at 4.2 K. With the disordered 7-nm-thick NbTiN films, we fabricated SNSPDs with system detection efficiency up to 83% for 1550 nm photons at 4.2 K. Moreover, these devices also show saturated intrinsic detection efficiency for 2000 nm photons. With the features outlined, the devices can be integrated into the idle 4.2 K stage of the dilution refrigerator for applications in optical quantum information processing or utilize for detecting laser radar signals in airborne platforms.

Intrusion detection system for controller area network

The rapid expansion of intra-vehicle networks has increased the number of threats to such networks. Most modern vehicles implement various physical and data-link layer technologies. Vehicles are becoming increasingly autonomous and connected. Controller area network (CAN) is a serial bus system that is used to connect sensors and controllers (electronic control units—ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. The goal of this research is to design, implement, and test an efficient and effective intrusion detection system for intra-vehicle CANs. Classic cryptographic approaches are resource-intensive and increase processing delay, thereby not meeting CAN latency requirements. There is a need for a system that is capable of detecting intrusions in almost real-time with minimal resources. Our research proposes a long short-term memory (LSTM) network to detect anomalies and a decision engine to detect intrusions by using multiple contextual parameters. We have tested our anomaly detection algorithm and our decision engine using data from real automobiles. We present the results of our experiments and analyze our findings. After detailed evaluation of our system, we believe that we have designed a vehicle security solution that meets all the outlined requirements and goals.

Performance analysis of various machine learning algorithms for CO2 leak prediction and characterization in geo-sequestration injection wells

The effective detection and prevention of CO2 leakage in active injection wells are paramount for safe carbon capture and storage (CCS) initiatives. This study assesses five fundamental machine learning algorithms, namely, Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), for use in developing a robust data-driven model to predict potential CO2 leakage incidents in injection wells. Leveraging wellhead and bottom-hole pressure and temperature data, the models aim to simultaneously predict the location and size of leaks. A representative dataset simulating various leak scenarios in a saline aquifer reservoir was utilized. The findings reveal crucial insights into the relationships between the variables considered and leakage characteristics. With its positive linear correlation with depth of leak, wellhead pressure could be a pivotal indicator of leak location, while the negative linear relationship with well bottom-hole pressure demonstrated the strongest association with leak size. Among the predictive models examined, the highest prediction accuracy was achieved by the KNNR model for both leak localization and sizing. This model displayed exceptional sensitivity to leak size, and was able to identify leak magnitudes representing as little as 0.0158% of the total main flow with relatively high levels of accuracy. Nonetheless, the study underscored that accurate leak sizing posed a greater challenge for the models compared to leak localization. Overall, the findings obtained can provide valuable insights into the development of efficient data-driven well-bore leak detection systems.

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.

MIDGNWD : Design of an efficient multimodal iterative deep graph network wearable interface for detecting drowsy drivers

Drowsy driving is a significant contributor to road traffic accidents, necessitating the development of robust and efficient detection systems. Existing approaches for detecting drowsy drivers often rely on single modalities such as image-based facial analysis or vehiclebased metrics, which may lack precision and be susceptible to environmental variations. Furthermore, the existing models might be computationally expensive and result in delayed responses. To address these limitations, this paper proposes a novel Multimodal Iterative Deep Graph Network Wearable Interface that leverages a rich set of data including heart rate, respiration, accelerometer movement, location (speed), traffic, and weather data. These data samples are processed through a fusion of different Recurrent Neural Network processes. The Bidirectional Long Short-Term Memory (BiLSTM) which is used to generate augmented features, is fused with Bidirectional Gated Recurrent Unit (BiGRU) which assists in estimating multidomain feature sets. These features are then used to train an efficient Deep Graph Recurrent Network (DGRN), which facilitates the real-time identification of drowsy drivers. The proposed model significantly enhances the precision of drowsy driver detection by 8.5%, accuracy by 9.5%, recall by 8.3%, and Area Under the Curve (AUC) by 4.9%, while also improving specificity by 5.5% and reducing delay by 10.4%. These improvements collectively contribute to a substantial enhancement in road safety, potentially saving lives by preventing accidents due to drowsy driving. The proposed system, with its real-time processing capability and high accuracy, stands as a significant improvement for identifying drowsy drivers, paving the way for safer and smarter transportation systems.

An efficient secure and energy resilient trust-based system for detection and mitigation of sybil attack detection (SAN).

In the modern digital market flooded by nearly endless cyber-security hazards, sophisticated IDS (intrusion detection systems) can become invaluable in defending against intricate security threats. Sybil-Free Metric-based routing protocol for low power and lossy network (RPL) Trustworthiness Scheme (SF-MRTS) captures the nature of the biggest threat to the routing protocol for low-power and lossy networks under the RPL module, known as the Sybil attack. Sybil attacks build a significant security challenge for RPL networks where an attacker can distort at least two hop paths and disrupt network processes. Using such a new way of calculating node reliability, we introduce a cutting-edge approach, evaluating parameters beyond routing metrics like energy conservation and actuality. SF-MRTS works precisely towards achieving a trusted network by introducing such trust metrics on secure paths. Therefore, this may be considered more likely to withstand the attacks because of these security improvements. The simulation function of SF-MRTS clearly shows its concordance with the security risk management features, which are also necessary for the network's performance and stability maintenance. These mechanisms are based on the principles of game theory, and they allocate attractions to the nodes that cooperate while imposing penalties on the nodes that do not. This will be the way to avoid damage to the network, and it will lead to collaboration between the nodes. SF-MRTS is a security technology for emerging industrial Internet of Things (IoT) network attacks. It effectively guaranteed reliability and improved the networks' resilience in different scenarios.

A deep learning framework for the early detection of multi-retinal diseases.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

Image encryption with leveraging blockchain-based optimal deep learning for Secure Disease Detection and Classification in a smart healthcare environment

<abstract> <p>Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODL-SDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques.</p> </abstract>

XAI-PDF: A Robust Framework for Malicious PDF Detection Leveraging SHAP-Based Feature Engineering

With the increasing number of malicious PDF files used for cyberattacks, it is essential to develop efficient and accurate classifiers to detect and prevent these threats. Machine Learning (ML) models have successfully detected malicious PDF files. This paper presents XAI-PDF, an efficient system for malicious PDF detection designed to enhance accuracy and minimize decision-making time on a modern dataset, the Evasive-PDFMal2022 dataset. The proposed method optimizes malicious PDF classifier performance by employing feature engineering guided by Shapley Additive Explanations (SHAP). Particularly, the model development approach comprises four phases: data preparation, model building, explainability of the models, and derived features. Utilizing the interpretability of SHAP values, crucial features are identified, and new ones are generated, resulting in an improved classification model that showcases the effectiveness of interpretable AI techniques in enhancing model performance. Various interpretable ML models were implemented, with the Lightweight Gradient Boosting Machine (LGBM) outperforming other classifiers. The Explainable Artificial Intelligence (XAI) global surrogate model generated explanations for LGBM predictions. Experimental comparisons of XAI-PDF with baseline methods revealed its superiority in achieving higher accuracy, precision, and F1-scores with minimal False Positive (FP) and False Negative (FN) rates (99.9%, 100%, 99.89%,0.000, and 0.002, respectively). Additionally, XAI-PDF requires only 1.36 milliseconds per record for predictions, demonstrating increased resilience in detecting evasive malicious PDF files compared to state-of-the-art methods

AI Empowers Data Mining Models for Financial Fraud Detection and Prevention Systems

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. This paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. Then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. The experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security.

Multisensory Fusion Approaches for Accurate Smoke Detection in Smart Environments

The reassessment of alarm systems’ role in this regard has led to the search for improved ways of detecting fire. In this study, sensor fusion is explored to improve the accuracy and reliability of smoke detection. Since individual sensors are limited in their capabilities, this research seeks to merge different sensor data using complex fusion techniques. This paper gives a detailed analysis of several types of sensors that are used indoors and outdoors as well as firefighter training grounds that have multiple fire sources. To work around this problem, the Adaboost algorithm was used as an ensemble learning technique where sensor data were combined iteratively to form a strong classification model. The study then goes on to meticulously plot variable distribution graphsbar charts, carry out correlation analyses, and make comparisons with other studies done previously; these findings give insight into how effective sensor fusion methods could be when it comes to smoke detection. The research results indicate that incorporating multiple sensors can significantly enhance detection accuracy and reliability. Thus, the findings obtained from this study identify a promising path for creating more efficient smoke detection systems.

A Mathematical Analysis of Feature Extraction Methodologies with Application on Leaf Disease Detection

This study compares novel and established feature extraction techniques for classifying leaf diseases. Conventional methods like Transfer Learning and DCT are contrasted with novel techniques like Segmentation using a heterogeneous data set of healthy and diseased leaf images. With little training data, transfer learning is beneficial, but with large data, more efficient algorithms may be more suitable, such as the custom segmentation model described in this work. The development of efficient plant disease detection systems using the information from these discoveries would improve agricultural practises and increase food security.

Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.

Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

Fabric defects cause both labor and raw material losses and energy costs. These undesirable situations negatively affect the competitiveness of companies in the textile sector. Traditionally, human-oriented quality control also has important limitations such as lack of attention and fatigue. Robust and efficient defect detection systems can be developed with image processing and artificial intelligence methods. This study proposes a deep learning-based method to detect and classify common fabric defects in circular knitting fabrics. The proposed method adds a fine-tuned mechanism to the MobileNetV2 deep learning model. The added fine-tuned mechanism is optimized to classify fabric defects. The proposed model has been tested on a fabric dataset containing circular knitting fabric defects. Obtained results showed that the proposed method produced desired results in fabric defect detection and classification.