Automatic Detection Technique Research Articles

Rice Leaf Diseases Classification Using Deep Learning Algorithms for Smartphone Application Development: An Empirical Study

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and [Formula: see text]-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models.

Vertical back movement of cows during locomotion: detecting lameness with a simple image processing technique.

This research paper proposes a simple image processing technique for automatic lameness detection in dairy cows under farm conditions. Seventy-five cows were selected from a dairy farm and visually assessed for a reference/real lameness score (RLS) as they left the milking parlor, while simultaneously being video-captured. The method employed a designated walking path and video recordings processed through image analysis to derive a new computerized automatic lameness score (ALDS) based on calculated factors from back arch posture. The proposed automatic lameness detection system was calibrated using 12 cows, and the remaining 63 were used to evaluate the diagnostic characteristics of the ALDS. The agreement and correlation between ALDS and RLS were investigated. ALDS demonstrated high diagnostic accuracy with 100% sensitivity and specificity and was found to be 100% accurate with a perfect agreement (ρc = 1) and strong correlation (r = 1, P < 0.001) for lameness detection in binary scores (lame/non-lame). Moreover, the ALDS had a strong agreement (ρc = 0.885) and was highly correlated (r = 0.840; 0.796-1.000 95% confidence interval, P < 0.001) with RLS in ordinal scores (lameness severity; LS1 to LS5). Our findings suggest that the proposed method has the potential to compete with vision-based lameness detection methods in dairy cows in farm conditions.

Automatic Detection and Identification of Calcareous Nannofossils in Chalk Using Deep Learning: A Proof-of-Concept Study for Biostratigraphy and Climate Research

Calcareous nannofossils serve as crucial indicators for establishing the biostratigraphic age of chalk macrofossil specimens in natural science collections. Better age control of specimens collected several hundred years ago enables us to uncover the dark data hidden within these collections and incorporate these data into current research projects, examining ecosystem response to past climate change. However, the manual identification of these microscopic organisms is laborious and subjective, and so we are harnessing deep learning techniques for automatic nannofossil detection and identification. This approach required the construction of a robust dataset, currently comprising over 100,000 labelled images, complemented by the development of multiple specialised deep learning models. While some models focus on detecting target species, others are dedicated to species classification. Evaluation on an independent test set showcases the efficacy of our methodology, with the current detection model achieving a balanced accuracy of 93%. Similarly, the classification model demonstrates robust performance, attaining an average balanced accuracy of 96%. Furthermore, as well as assisting with our biostratigraphic studies, the dataset of accurately labelled images has enabled us to test other aspects of ecosystem response. For example, examining morphometric changes in nannofossils over geological time can provide valuable insights into the potential impact of current global warming on modern phytoplankton assemblages (Mancini et al. 2021). This is particularly important for coccolithophores (Young et al. 2005), which play a critical role as primary producers in the global carbon cycle. A decrease in their size could lead to bottom-up ecosystem impacts and reduced carbon sequestration (Poulton et al. 2007, Krumhardt et al. 2017). Using our dataset, we conducted a deep-learning-enhanced automatic morphometric analysis focusing on the nannofossil species, Tranolithus orionatus. Our analysis revealed that two key morphometric parameters, minor axis and area size, showed statistically significant differences between the Cenomanian stage (approximately 100.5 to 93.9 million years ago) and the post-Cenomanian stages of the Late Cretaceous (approximately 93.9 to 66.0 million years ago). Kolmogorov-Smirnov tests (Massey 1951) between the two samples yielded p-values of 0.039 for the minor axis and 0.031 for the area size. Understanding these morphometric changes is crucial due to the close parallels between current climate projections and the warming and greenhouse climate of the Late Cretaceous, particularly the Cenomanian-Turonian boundary event (Arthur et al. 1990). Insights into how organisms changed morphologically during past periods of environmental stress can help us more effectively predict future responses of organisms under similar conditions (Razmjooei et al. 2020). These findings underscore the effectiveness of our approach in automating the identification and recognition of chalk nannofossils, helping to unlock natural science collections and to address key questions related to marine response to past climate change.

LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

SQL Injection Attack Detection Based on Similarity Matching Between Vectors Extracted From Design Time and Run-Time Queries

Everyone uses web-based applications to carry out daily business and personal tasks. These programmes are vulnerable to attack by hackers, who may also misuse the data. The most serious attack with the greatest damaging potential on digital platforms is the structured query language injection attack (SQLiA). The backend databases could be corrupted or destroyed by SQLiA if it manages to breach security protections. Using SQLiA tactics, hackers can get unauthorized access, steal important data, and take over the network completely or partially. An automatic SQL injection prevention and detection technique is needed to safeguard web-based applications from SQLiA. This research suggests a novel similarity-matching algorithm of vectors extracted from design time and run-time queries. This technique allocates the weights of different SQL keywords used in design time and run-time queries and further design time and run-time vectors have been created from respective queries. The similarity between the design time and run time vector is determined by calculating the angle between these two vectors. The angle of deviation between the design time vector and run time vector is calculated and if the angle of deviation is zero, then it is concluded as no SQL injection otherwise, it indicates the existence of SQLiA vulnerability. The proposed algorithm is validated against the GitHub dataset. In the first dataset, out of 1300 injected queries, the proposed method identifies 1219 injected queries; out of 300 normal queries, it identifies 290 normal queries with 93.76% and 96.66% detection accuracy, respectively. Similarly, for the second dataset, out of 10489 injected queries, it identifies 10280 injected queries and out of 301 normal queries, it identifies 280 normal queries with 98.01% and 93.02% detection accuracy, respectively.

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology

BackgroundIn this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).MethodsMultiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation.ResultsThe CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN’s trained with mpMRI and parametric clinical and the CNN’s trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28.ConclusionThe results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring.Trial registrationThe trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.

Line to Ground Fault Detector in 400 KV Transmission Line by Using Intelligent System

The ability to locate and identify problems in power transmission lines is the most crucial component of power system engineering since it ensures the stable and efficient operation of electrical grids. to minimize downtime, prevent cascading failures, and maintain the power system's stability. problem localization helps identify the precise location of the problem and speeds up the process of restoring the power supply while minimizing the impact on consumers. This paper presents a measurement-based automatic fault localization and detection (ANFIS) technique for transmission lines. High-speed processing of real-time error localization and detection is made possible by the design and implementation of ANFIS. It is suggested that ANFIS for digital distance protection systems be able to discover issues in addition to detecting them. When a transmission experiences a three-phase malfunction Thus, the suggested method can precisely pinpoint the impacted stages. ANFIS has been trained and tested using many kinds of field data. The field data are collected by simulating faults in the Simulink Matlab model depicting the transmission line at various sites between Misan – Kut station 400 Kv along 200 Km using computer software based on Matlab. Phase current and voltage measurements are utilized as ANFIS inputs and are available at the busses. Regarding the kind and detection of defects, The output will demonstrate fault and location identification with an extremely low error percentage through simulated processes; the results also demonstrate that the approach's selectivity and speed are quite dependable and provide adequate performance for applications involving transmission and distribution monitoring; and security. The study emphasizes the criticality of promptly and precisely locating faults in power transmission lines to preserve the stability and security of the power system.

Augmenting Telepostpartum Care With Vision-Based Detection of Breastfeeding-Related Conditions: Algorithm Development and Validation.

Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out. This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers. We evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models' ability to distinguish between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model. The best model achieves an average area under the receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and images depicting multiple conditions in the same breast. This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes.

Enhancing Medical Image Classification with an Advanced Feature Selection Algorithm: A Novel Approach to Improving the Cuckoo Search Algorithm by Incorporating Caputo Fractional Order.

Glaucoma is a chronic eye condition that seriously impairs vision and requires early diagnosis and treatment. Automated detection techniques are essential for obtaining a timely diagnosis. In this paper, we propose a novel method for feature selection that integrates the cuckoo search algorithm with Caputo fractional order (CFO-CS) to enhance the performance of glaucoma classification. However, when using the infinite series, the Caputo definition has memory length truncation issues. Therefore, we suggest a fixed memory step and an adjustable term count for optimization. We conducted experiments integrating various feature extraction techniques, including histograms of oriented gradients (HOGs), local binary patterns (LBPs), and deep features from MobileNet and VGG19, to create a unified vector. We evaluate the informative features selected from the proposed method using the k-nearest neighbor. Furthermore, we use data augmentation to enhance the diversity and quantity of the training set. The proposed method enhances convergence speed and the attainment of optimal solutions during training. The results demonstrate superior performance on the test set, achieving 92.62% accuracy, 94.70% precision, 93.52% F1-Score, 92.98% specificity, 92.36% sensitivity, and 85.00% Matthew's correlation coefficient. The results confirm the efficiency of the proposed method, rendering it a generalizable and applicable technique in ophthalmology.

Accident Detection and Nearby Patrol Alert System Using AI &amp;ML

Abstract: Intelligent communities are utilizing different creative ideas to improve the quality of human life. Due to fast growing sizes of our cities, need of travelling is constantly increasing, which in turn has increased count of vehicles on the roads. Increasing number of vehicles on the roads has brought about numerous difficulties for Street Traffic Management Authorities. Amongst different traffic related issues, road accidents are something worth giving attention to and have to be on the priority list. This project describes various automatic road accident detection techniques, which automatically detect accidents using surveillance videos in real-time and intimates the event scenario to control room and the main uniqueness of the system is it automatically estimates the crash value of the incident and intimates to the user. The proposed method assumes that traffic accident events are described by visual features occurring through a temporal way. Therefore, a visual features extraction phase, followed by a temporary pattern identification, compose the model architecture. The visual and temporal features are learned in the training phase through convolution and recurrent layers using built-from-scratch and public datasets. An accuracy of 98% is achieved in the detection of accidents in public traffic accident datasets, showing a high capacity in detection independent of the road structure.

High-Throughput Multitarget Molecular Detection in an Automatic Light-Addressable Photoelectrochemical Sensing Platform.

Successively emerged high-throughput multitarget molecular detection methods bring significant development tides in chemical, biological, and environmental fields. However, several persistent challenges of intricate sample preparation, expensive instruments, and tedious and skilled operations still need to be further addressed. Here, we propose an automatic light-addressable photoelectrochemical (ALA-PEC) sensing platform for sensitive and selective detection of multitarget molecules. With Au nanoparticle-decorated TiO2 nanotube photonic crystals (Au-TiO2 NTPCs) as a photoelectrode and 8 kinds of antibiotics as target molecules, the ALA-PEC sensing system implements automatic detection of multimolecules in a short time with high sensitivity and good selectivity. Random samples with different amounts of antibiotics have been well-distinguished in the ALA-PEC system, and both the chemical components and concentrations have been well-illustrated in a pattern recognition model. It is worth noting that 8 samples are not the limit of the ALA-PEC sensing platform, which can be easily expanded to more complex detection arrays based on practical needs. The emerging ALA-PEC sensing platform provides a new solution for rapid screening and detection of multitarget and high-throughput substances and potentially brings the automatic, portable, sensitive, high-throughput, and cost-effective detection technique to an entire new realm.

Automated assessment of hydrostatic pressure resistance in woven fabrics using the enhanced YOLOv8 network

Hydrostatic pressure resistance serves as a crucial metric in the assessment of water resistance in woven fabrics. The expeditious, precise, and efficient conduct of hydrostatic pressure tests holds paramount importance in advancing the progress and production of high-performance textiles. Addressing the challenges posed by intricate printed patterns on woven fabrics, and the presence of small, widely scattered water droplets, the study leverages the enhanced YOLOv8 model to develop a machine vision-based automated detection technique for assessing water resistance in woven fabric. The proposed method incorporates convolutional block attention module attention mechanisms into the backbone and neck network, replaces the path aggregation network structure of YOLOv8 with the bidirectional feature pyramid network structure, and introduces a dedicated detection head for small targets. These enhancements facilitate accurate identification of water outlet points on the woven fabric and precise recording of frame positions, enabling the precise measurement of hydrostatic pressure. Validation of the proposed model is conducted through a series of comparative experiments utilizing a self-collected dataset. The experimental results underscore the exemplary performance of the proposed model, evidenced by an AP0.5 score of 92.18%, showcasing superior overall efficacy in comparison with alternative models. Notably, the target localization time error is found to be less than 2 s when contrasted with manual detection. This method substantially enhances the accuracy of water droplet detection and localization in hydrostatic pressure resistance testing of woven fabric, characterized by complex surface patterns, thereby contributing to refinement of hydrostatic pressure testing methodologies in woven fabric analysis.

Automated Foreign Object Detection for Carbon Fiber Laminates Using High-Resolution Ultrasound Testing.

Carbon fiber laminates have become popular in the manufacturing industry for their many desirable properties, like good vibration damping, high strength-to-weight ratio, toughness, high dimensional stability, and low coefficient of thermal expansion. During the manufacturing process, undesirable foreign objects, such as peel-ply strips, gloving material, and Kapton film, can be introduced into the part which can lead to a localized weakness. These manufacturing defects can function as stress concentration points and oftentimes cause a premature catastrophic failure. In this study, a method using high-resolution pulse-echo ultrasound testing is employed for the detection and quantification of the dimensions of foreign object debris (FOD) embedded within carbon fiber laminates. This research presents a method to create high-resolution C-scans using an out of immersion tank portable housing ultrasound scanning system, with similar capabilities to that of a full immersion system. From the full-waveform dataset, we extract the FOD depth and planar dimensions with an automatic edge detection technique. Results from several carbon fiber laminates are investigated with embedded foreign objects that are often considered undetectable. Results are presented for FOD identification for two different shapes: circles with diameters ranging from 7.62 mm to 12.7 mm, and 3-4-5 triangles with hypotenuses ranging from 7.6 mm to 12.7 mm. CT imaging is used to confirm proper FOD placement and that the FOD was not damaged or altered during manufacturing. Of importance for the ultrasound inspection results, in every single case studied, the FOD is detected, the layer depth is properly identified, and the typical error is less than 1.5 mm for the primary dimension.

Application of neuroimaging in diagnosis of focal cortical dysplasia: A survey of computational techniques

Focal Cortical Dysplasia (FCD) is a neurodevelopmental disorder characterized by abnormal neuronal migration, differentiation, and maturation, resulting in a range of clinical symptoms including drug-resistant epilepsy. Accurate and timely diagnosis of FCD is essential for effective treatment and management of patients with this condition. In recent years, there have been significant advances in neuroimaging and machine learning techniques, enabling automated detection of FCD lesions with increasing accuracy and efficiency.In this survey article, we provide an overview of the current state-of-the-art in computational techniques for the automated detection of FCD lesions using neuroimaging. We first introduce some common imaging findings that radiologists look for in FCD lesions, then review the automatic detection techniques studied in the last decade. These techniques have shown promising results in detecting and localizing FCD lesions, improving diagnostic accuracy and reducing misdiagnoses, diagnosis time, and cost of care.We suggest that the use of these types of quantitative image analysis tools and algorithms can play a key role in improving the overall management and outcome of patients with FCD. However, further studies are needed to validate and optimize the performance of these techniques in clinical practice. In summary, the combination of advanced neuroimaging and computational techniques has the potential to revolutionize the diagnosis and treatment of FCD, leading to better outcomes for patients with this condition.

Regional landslide mapping model developed by a deep transfer learning framework using post-event optical imagery

ABSTRACT Landslides are major natural disasters in mountainous areas, often caused by earthquakes and heavy rainfalls. Traditional manual delineation methods for identifying landslide features using optical imagery are inefficient, highlighting the need for automated detection techniques. Deep Convolutional Neural Networks (CNNs) have emerged as advanced solutions in computer vision for this purpose. Despite the reliance on pre-event and post-event imagery or various data sources like digital elevation models (DEMs), the success of deep learning models largely depends on the quality and availability of training data. This poses a challenge for their immediate application after a landslide. This study explores the transferability of a CNN model trained on data from the 2016 Kumamoto Earthquakes for detecting landslides in different events, specifically the 2018 Hokkaido earthquake and the 2017 Asakura Rainfall in Japan. These cases were chosen for their geographical similarities. The proposed deep transfer learning model, based on a DeepLabV3 + architecture built on a pre-trained ResNet50, automatically identifies landslide features without needing specific training data or model adjustments for each event. It achieved high accuracy in both cases, demonstrating CNNs’ potential for broad application in landslide detection and enhancing disaster response efforts.

A robust multimodal detection system: physical exercise monitoring in long-term care environments.

Falls are a major cause of accidents that can lead to serious injuries, especially among geriatric populations worldwide. Ensuring constant supervision in hospitals or smart environments while maintaining comfort and privacy is practically impossible. Therefore, fall detection has become a significant area of research, particularly with the use of multimodal sensors. The lack of efficient techniques for automatic fall detection hampers the creation of effective preventative tools capable of identifying falls during physical exercise in long-term care environments. The primary goal of this article is to examine the benefits of using multimodal sensors to enhance the precision of fall detection systems. The proposed paper combines time-frequency features of inertial sensors with skeleton-based modeling of depth sensors to extract features. These multimodal sensors are then integrated using a fusion technique. Optimization and a modified K-Ary classifier are subsequently applied to the resultant fused data. The suggested model achieved an accuracy of 97.97% on the UP-Fall Detection dataset and 97.89% on the UR-Fall Detection dataset. This indicates that the proposed model outperforms state-of-the-art classification results. Additionally, the proposed model can be utilized as an IoT-based solution, effectively promoting the development of tools to prevent fall-related injuries.

From Drips to Data: Preventing Unnecessary Leakages in Water Distribution Networks in Slovakia

Water, a critical resource, suffers from significant losses due to leakages in Water Distribution Networks (WDN). These losses present financial, environmental, and public health challenges. With water utility companies amassing vast amounts of data from enterprise information systems, the opportunity for data-driven leakage detection arises. With the increased use of enterprise information systems, water utility companies are generating vast amounts of data that can be valuable for predicting or detecting water leaks early and also for the development of new, automatic, and effective data-driven leak detection techniques. The presented study utilizes this data, by applying anomaly detection methods to heterogeneous time-series data from various components of the WDN in Slovakia. Our results demonstrate that components of the network, such as measured power consumption, water source temperature, and water source levels show significant positive associations with faults in the Water Distribution Network.

Integration of artificial intelligence and augmented reality for assisted detection of textile defects

The Fourth Industrial Revolution conceptualizes the rapid change of industries resulting from the convergence of technologies such as artificial intelligence, genetic editing, and advanced robotics that enable increasing interconnectivity and machines that can analyze and diagnosing problems without human intervention, through intelligent automation. In this scenario, the use of augmented reality technologies is of great interest. The paper aims to explore the use of augmented reality in support of traditional inspections for assisting textile experts in fabric defect detection. The contribution of this study consists of three main phases, necessary for the future development of the system: (1) the analysis of possible automatic defect detection techniques; (2) the analysis of hardware solutions for the realization of a system based on important criteria such as operator comfort, system footprint, and so on; (3) the proposal of a possible comprehensive solution. Considering these aspects this paper identifies and investigate the best scenario for the introduction of artificial intelligence and augmented reality technologies to help the operator in the detection of textile defects.

Investigating the effect of distance on the implementation of RCNN automatic detection technique to the human body

The identification of humans constitutes a crucial component of monitoring systems, given the significance of the timely detection of individuals. Despite advancements in people detection systems, detecting humans at long distances remains challenging. In this study, we employed the Region-based Convolutional Neural Network (RCNN) approach to training a system on images captured at varying distances between the camera and individuals. The results demonstrate promising outcomes, with the system achieving a maximum detection recall of 1 for identifying people at distances of up to 40 meters and maximum precision of 1 for identifying people at distances of up to 50 meters.

LISF: A Security Framework for Internet of Things (IoT) Integrated Distributed Applications

Distributed applications where Internet of Things (IoT) technology integrated are vulnerable to different kinds of attacks. Machine learning algorithms are widely used to detect intrusions in such applications. However, there is need for an effective unsupervised learning approach which can detect known and also unknown attacks. Towards this end, in this paper, we proposed a framework to protect security of IoT integrated architectures that are distributed in nature. Our framework is named Learning based IoT Security Framework (LISF). The framework is designed to have machine learning based security to IoT integrated use cases. Since IoT networks cause network traffic that is to be monitored and protected from external attacks, the proposed system uses deep learning technique for automatic detection of cyber-attacks. Particularly, the system exploits deep autoencoder which comprises of encoder and decoder for automatic detection of different kinds of intrusions. It is based on unsupervised learning which is crucial for distributed environments where network flows cannot have sophisticated training samples. We proposed an algorithm named Deep Autoencoder based Cyber Attack Detection (DAE-CAD). Experiments are made using IoT use case dataset known as UNSW-NB15. Our empirical results revealed that DAE-CAD outperforms existing methods with highest accuracy 91.36%.