Minimum False Positive Rate Research Articles

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

Certain Investigations on Feature Selection Technique Using Artificial Immune Systems for EEG Color Visualization Classification

This paper aims to extract and select the significant features of electroencephalogram (EEG) signals and classify the visual stimulation of distinct colors. In this work, a novel method for selecting distinct colors using EEG signals called affinity artificial immune and Daubechies wavelet time-based learning (AAIDWTL) is proposed. Initially, the EEG signals were collected in a controlled environment and an in-built band-pass filter was applied to remove the artifacts. The filtered signals were converted into frequency domain signals using least squarebased short-term Fourier transform. After that, by utilizing Daubechies wavelet statistical time-based feature extraction model the time domain features were extracted. Followed by, computationally efficient features were selected using an affinity artificial immune-based feature selection model. The selected features were classified using a polynomial kernel multiclass classification-based machine learning algorithm and achieved an accuracy of 97.5% when compared with other methods like linear discriminant analysis (LDA) which obtained only 92%. Furthermore, while utilizing the proposed method classification time was considerably less when compared to LDA. The experimental result shows that the proposed color stimulation of the EEG signals method achieved greater improvement in terms of both classification time and classification accuracy with a minimum false positive rate.

An Efficient Cyber Assault Detection System using Feature Optimization for IoT-based Cyberspace

With the exponential growth in connected smart devices that interchange sensitive, crucial, and personal data over the Internet of Things (IoT)-based cyberspace, IoT becomes undefended to cyber assaults. Intrusion Detection System (IDS) is a vital segment of security tool to detect cyber assaults. However, IDS produce high False Positive Rate (FPR) because IDS improves accuracy at the same time as it increases FPR. Therefore, a more effective cyber assaults detection system is needed. Based on the aforementioned issues, this paper proposes an approach to recognizing cyber assaults in the IoT environment. This approach first performs various data pre-processing steps. Next, important features are optimized using modified Binary Pigeon Inspired Optimizer. After that, optimized feature subset is fed separately to the monolithic classifiers such as k-Nearest Neighbours (kNN), Support Vector Machine (SVM), Decision Tree (DT) and bagging-based ensemble model such as Random Forest (RF) for detection of cyber assaults. In this study, UNSW-NB15 is used to test the success of the system, and related existing methods are compared with these results. Based on the results of the experiment, the proposed system with RF is the most effective in terms of accuracy (99.41%), F1-score (99.33%), detection rate (99.09%), and precision (99.92%) with a minimum FPR (0.03%) for a subset of optimized features (4 independent features out of 42).

Adam teaching learning optimization enabled LeNet for autism spectrum disorder detection using brain MRI

The autism spectrum disorder (ASD) is a complicated, lifetime, neuro-developmental circumstance of highly unknown causes. It is greatly more ordinary than formerly believed, frequency only to mental retardation amongst serious developmental disorders. Even though, a heritable element is demonstrated in the etiology of ASD, reputed risk genes are still to be detected. Therefore, Adam Teaching Learning Optimization-LeNet (ATLO-LeNet) is introduced for the detection of ASD utilizing brain Magnetic Resonance Imaging (MRI). Here, input considered is provided for the image pre-processing, wherein extraction of Region of Interest (ROI) is performed and median filter is used to eradicate noises. Then, extraction of the pivotal region is completed by the devised ATLO and hence, output-1 is obtained. On the other side, features are mined from an input MRI image. The extracted features include statistical features as well as texture features. From extracted features, output-2 is attained. By considering the output-1 and output-2, ASD classification is completed by LeNet. The LeNet is trained by ATLO, which is the amalgamation of the Adam algorithm and Teaching–Learning-Based Optimization (TLBO). Moreover, ATLO-LeNet acquired a maximum accuracy of 0.916, Negative Predictive Value (NPV) of 0.916and Positive Predictive Value (PPV) of 0.917 whereas it attained a minimum False Positive Rate (FPR) of 0.132 and False Negative Rate (FNR) of 0.152.

An Enhanced Minimax Loss Function Technique in Generative Adversarial Network for Ransomware Behavior Prediction

Recent ransomware attacks threaten not only personal files but also critical infrastructure like smart grids, necessitating early detection before encryption occurs. Current methods, reliant on pre-encryption data, suffer from insufficient and rapidly outdated attack patterns, despite efforts to focus on select features. Such an approach assumes that the same features remain unchanged. This approach proves ineffective due to the polymorphic and metamorphic characteristics of ransomware, which generate unique attack patterns for each new target, particularly in the pre-encryption phase where evasiveness is prioritized. As a result, the selected features quickly become obsolete. Therefore, this study proposes an enhanced Bi-Gradual Minimax (BGM) loss function for the Generative Adversarial Network (GAN) Algorithm that compensates for the attack patterns insufficiency to represents the polymorphic behavior at the earlier phases of the ransomware lifecycle. Unlike existing GAN-based models, the BGM-GAN gradually minimizes the maximum loss of the generator and discriminator in the network. This allows the generator to create artificial patterns that resemble the pre-encryption data distribution. The generator is used to craft evasive adversarial patterns and add them to the original data. Then, the generator and discriminator compete to optimize their weights during the training phase such that the generator produces realistic attack patterns, while the discriminator endeavors to distinguish between the real and crafted patterns. The experimental results show that the proposed BGM-GAN reached maximum accuracy of 0.98, recall (0.96), and a minimum false positive rate (0.14) which all outperform those obtained by the existing works. The application of BGM-GAN can be extended to early detect malware and other types of attacks.

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%.

IIoT-IDS Network using Inception CNN Model

Modern network and Industrial Internet of Things (IIoT) technologies are quite advanced. Networks experience data breaches annually. As a result, an Intrusion Detection System is designed for enhancing the IIoT security protection under privacy laws. The Internet of Things' structural system and security performance criteria must meet high standards in an adversarial network. The network system must use a system that is very stable and has a low rate of data loss. The basic deep learning network technology is picked after analysing it with a huge number of other network configurations. Further, the network is upgraded and optimised by the Convolutional Neural Network technique. Additionally, an IIoT anti-intrusion detection system is built by combining three network technologies. The system's performance is evaluated and confirmed. The proposed model gives a better detection rate with a minimum false positive rate, and good data correctness. As a result, the proposed method can be used for securing an IIoT data privacy under the law.

Optimization of patient-based real-time quality control based on the Youden index

AimThis study sets out to investigate the utility of exponentially weighted moving average (EWMA) as patient-based real-time quality control (PBRTQC) by conducting a simulation study and subsequent real-patient data implementation to determine optimal EWMA features (weighting factors, control limits, and truncation methods) based on the Youden index. MethodsA simulation experiment was conducted in the first stage to investigate optimal EWMA features for the tests, including aspartate aminotransferase, blood urea nitrogen, and glucose, calcium, creatinine, potassium, sodium, triglycerides, thyroid - stimulating hormone (TSH), and vitamin B12 tests. In the second stage of the study, EWMA was applied to real patient data to elucidate practical utility and achieve final optimal EWMA features. Different degrees of systematic errors (SE) including total allowable error (TEa) as a maximum error level were added to both simulation and patient results, and then the EWMA performance was assessed for different EWMA features. We calculated Youden’s index for each combination of EWMA features to find their optimal features to achieve minimum false positive rate (FPR) and maximum error detection rate at the SE level corresponding to TEa. ResultsEWMA implementation on real patient data revealed optimal EWMA features for each test. FPR values of creatinine and glucose were 18.48% and 10.17%, respectively, which exceeded the acceptable criteria for FPR (10%). The remaining six analytes showed acceptable FPR. ConclusionsWe showed the implementation of EWMA as PBRTQC, and optimization of its features based on the Youden index by conducting extensive performance evaluations and simulations in this study.

Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

An Intelligent RPL attack detection using Machine Learning-Based Intrusion Detection System for Internet of Things

The Internet of Things (IoT) is an important and a major key component to support human life. Sensors are the sensing devices that play an important role in IoT to detect environmental conditions. The significance of IoT is increasing due to the increase in the things connected to the internet using Internet Protocol version-6 (IPv6). Low-Power and Lossy Network (LLN) Routers used in IoT environment have limited processing, memory, and energy resources and hence it can't use the traditional routing protocols such as Open Shortest Path First (OSPF), Routing Information Protocol (RIP), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing(DSR) etc. However, Routing Protocol for Low power and Lossy Networks (RPL) is one of the prominent protocols which are developed to overcome the above issues but it could be exposed to specific types of attacks such as decreased rank attack, black hole attack, sinkhole attack, selective forwarding attack and version attacks, etc. Hence, it is necessary to develop a mechanism to detect and prevent such attacks. In this paper, efficient machine learning based Intrusion Detection System for Internet of Things is proposed to monitor the network activities against attacks and to detect the intruders more efficiently. This proposed machine learning based detection model performs feature selection and classification based on two new algorithms proposed in this paper called genetic recursive feature selection algorithm and fuzzy k-nearest neighbor classifier and detects the aforementioned attacks with maximum accuracy and minimum false positive rate. This work is also investigated on other performance metrics like precision, recall and F1-score to show the performance comparison between this proposed work with genetic recursive feature selection and fuzzy k-nearest neighbor classification algorithms and the existing works on intrusion detection.

Minimized False Alarm Predictive Threshold for Cloud Service Providers

Background: Cloud Security is a strong hindrance which discourages organisations to move toward cloud despite huge benefits. Distributed denial of service attacks operated via distributed systems compromise availability of cloud services which cause limited resources for authentic users and high expense for cloud service users and business owners. Objective: Techniques to identify distributed denial of service attacks with minimized false positives are highly required to ensure availability of cloud services to genuine users. Scarcity of solution which can detect denial of service attacks with minimum false positives and reduced detection delay has motivated us to compare classification algorithms for detection of distributed denial of service attacks with minimum false positive rate. Methods: Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. We designed a performance tuned support vector machine algorithm with features of F-hold cross validation strategy. Results: F-hold crosses validation strategy, which can detect denial of service packets with 99.89% accuracy. Conclusion: This system ensures economic sustainability for business owners and limited resources mitigation for authenticated and valid cloud users.

RETRACTED ARTICLE: Prewitt Logistic Deep Recurrent Neural Learning for Face Log Detection by Extracting Features from Images

Face log detection (FLD) in the surveillance video extracts a new face image from the video sequences (VS). FLD utilizes biometric techniques for humans’ recognition. To improve the precise FLD with less complexity of our proposed method is Prewitt Logistic Deep Recurrent Neural Learning (PLDRNL) used. The input VS was received from the video database. Next, the keyframes are extracted from the VS. This proposed deep recurrent neural learning method uses four hidden layers to remove the facial features such as the face, eyes, nose, and mouth in the form of an edge. The edges of each element are derived using the Prewitt edge detector through the horizontal and vertical mask. Finally, the relevant features are fed into the output layer. The PLDRNL uses a logistic activation function at the output layer for matching the extracted related elements with the pre-stored testing feature vector. If two features are matched, then the face in the given VF is detected. The error in the FD is minimized using gradient descent function at the output layer. Based on the results, the human face effectively identified with the minimum false-positive rate (FPR). Experimental evaluation is carried out using different factors such as FLD, FPR, and time complexity.

Active contour model with adaptive weighted function for robust image segmentation under biased conditions

The segmentation of images under biased conditions such as low contrast, high-intensity inhomogeneity, and noise is a challenge for any image segmentation model. The ideal image segmentation model must be capable of segmenting images with maximum accuracy and a minimum false-positive rate under biased conditions. In this paper, we propose a region-based active contour model (ACM), called global signed pressure and K-means clustering based on local correntropy with the exponential family (GSLCE), to address segmentation challenges under biased conditions. An adaptive weighted function is formulated based on the global and local image differences such that a single weighted function can drive both the global and local intensities. Further, the Riemannian steepest descent method is used for convergence of the proposed GSLCE energy function, and a Gaussian kernel is applied for spatial smoothing to obviate the computationally expensive level-set re-initialization. The experimental results show that, compared with state-of-the-art ACMs, the proposed GSLCE model obtained the best visual image segmentation results for synthetic and real images under biased conditions. Further, the qualitative and quantitative experimental results validate that the proposed model outperforms the state-of-the-art ACMs by yielding higher values of performance metrics. Moreover, the proposed GSLCE model requires substantially less processing time compared to the state-of-the-art ACMs.

The detection of lung cancer using massive artificial neural network based on soft tissue technique

BackgroundA proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CAD scheme recognizes non subtle nodules using x-ray images.MethodSuch an issue has been resolved by creating MANN (Massive Artificial Neural Network) based soft tissue technique from the lung segmented x-ray image. A soft tissue image recognizes nodule candidate for feature extortion and classification. X-ray images are downloaded using Japanese society of radiological technology (JSRT) image set. This image set includes 233 images (140 nodule x-ray images and 93 normal x-ray images). A mean size for a nodule is 17.8 mm and it is validated with computed tomography (CT) image. Thirty percent (42/140) abnormal represents subtle nodules and it is split into five stages (tremendously subtle, very subtle, subtle, observable, relatively observable) by radiologists.ResultA proposed CAD scheme without soft tissue technique attained 66.42% (93/140) sensitivity and 66.76% accuracy having 2.5 false positives per image. Utilizing soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) and accuracy is 72.96% at one false positive rate).ConclusionIn particular, a proposed CAD system determine sensitivity and accuracy in support of subtle nodules (sensitivity is 14/42 = 33.33% and accuracy is 33.66%) is statistically higher than CAD (sensitivity is 13/42 = 30.95% and accuracy is 30.97%) scheme without soft tissue technique. A proposed CAD scheme attained tremendously minimum false positive rate and it is a promising technique in support of cancerous recognition due to improved sensitivity and specificity.

Multi-keyword score threshold and B+ tree indexing based top-K query retrieval in cloud

Cloud computing is an emerging technology where computing resources are delivered as a service over a network which is accessed by many cloud users. Cloud services on the real-world application attain the fundamental resource sharing and low-cost preserving characteristics. While increasing the number of user requests, the most significant deal is the identification and retrieval of top-k queries in cloud environments. Several techniques have been developed to retrieve the top-k queries, but effective modeling of query result retrieval on cloud services with less complexity is not attained. In order to improve the query result retrieval rate, Top-k Query Multi-Keyword Score Threshold (Top-k QMKST) technique is developed. This technique considers four processes for retrieving the top-k results in minimum time. At first, multiple keywords are extracted from the query, and then the B+ tree indexing is used to index the data with the objective of reducing the response time and space complexity. Third, a score value is calculated using Kullback–Leibler Divergence which provides the probable results of keywords occurrences among a collection of keywords in an index list. At last Monotonic weighted score aggregation function is used for assigning the weight to the resultant content score. Experimental evaluation is carried out with different parameters and the results showed that the Top-k QMKST technique is better in case of query result retrieval with minimum false positive rate, reduced response time and space complexity.

DBFS: Dragonfly Bayes Fusion System to detect the tampered JPEG image for forensic analysis

Due to the advancement of a variety of photo editing and image processing software, image forensics analysis has become an important research topic in recent years. Numbers of research works are presented for image forensic analysis. Accordingly, this paper proposes a method named as Dragonfly Bayes Fusion System (DBFS) by integrating the Naive Bayes (NB) classifier and the Dragonfly optimization for detecting the tampered Joint Photographic Experts Group image. Initially, the input image is applied to the existing six forensic tools separately, and the classified binary map is generated. Then, the proposed DBFS fuses these decisions for generating the optimal decision. Here, the NB classifier creates the model by finding the mean and variance of every feature and this model is given as input to the Dragonfly optimization for optimally generating the probabilistic measures. Finally, the posterior probability of each feature is determined with respect to the tampered class, and the original class and the tampered image block is determined. The performance of the proposed system is evaluated with the existing methods, such as fuzzy theory based classification, rule-based classification, average method, and weighted average method for the evaluation metrics accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). The experimental results show that the proposed system outperforms the existing methods by obtaining the maximum accuracy of 0.9519, minimum FPR of 0.0490, and maximum TPR of 0.8720 when compared to the existing methods.

Privacy preserving proof of ownership for data in cloud storage systems

Storing data in cloud has become a necessity as users are accumulating abundant data every day and they are running out of physical storage devices. But majority of the data in the cloud storage is redundant. Data deduplication using convergent key encryption has been the mechanism popularly used to eliminate redundant data items in the cloud storage. Convergent key encryption suffers from various drawbacks. For instance, if data items are deduplicated based on convergent key, any unauthorized user can compromise the cloud storage by simply having a guessed hash of the file. So, ensuring the ownership of the data items is essential to protect the data items. As cukoo filter offers the minimum false positive rate, with minimal space overhead our mechanism has provided the proof of ownership.

Bridging Communication Gap Among People with Hearing Impairment: An Application of Image Processing and Artificial Neural Network

Before the present study, no sign language recognition system for the Nigeria indigenous sign language particularly Yoruba language has been developed. As a result, this research endeavors at introducing a Yoruba Sign Language recognition system using image processing and Artificial Neural Network (ANN).The proposed system (YSLRS) was implemented and tested. 600 images from 60 different signers were gathered. The images were acquired using vision based method, the different signers were asked to stand in front of a laptop’s camera make sign number from one to ten with their fingers in three different times and the images were stored in a folder. The image dataset was pre-processed for proper presentation for de-noising, segmentation and feature extraction. Thereafter, pattern recognition was done using feed forward back propagation ANN. The study revealed that Median filter with higher PSNR of 47.7 a lower MSE of 1.11, performed better than the Gaussian filter. Furthermore, the efficiency of the developed system was determined using mean square error and the best validation performance occurred at 25 epochs with a MSE of 0.004052, implying than ANN was able to adequately recognize the pattern of the Yoruba signs. Histogram was also used to determine the efficiency of the system, it can be seen that the histogram of the trained, tested and validated error bars were close to zero error, implying that the ANN and Receiver Operating Characteristic (ROC) was used to evaluate the performance of ANN in matching the features of the Yoruba Signs, which shows that ANN performed efficiently, having a high true positive rate and a minimum false positive rate. Finally, YSLRS developed in the study would reduce negative attitudes of victimizations suffered by the hearing-impaired individuals, by bridging communication gap among Nigerian PWD with hearing impairment.