Search results for: fraud prevention and detection

Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

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we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Wittawat Wasusathien, Samran Santalunai, Thanaset Thosdeekoraphat, Chanchai Thongsopa

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This paper presents breast cancer detection by observing the specific absorption rate (SAR) intensity for identification tumor location, the tumor is identified in coordinates (x,y,z) system. We examined the frequency between 4-8 GHz to look for the most appropriate frequency. Results are simulated in frequency 4-8 GHz, the model overview include normal breast with 50 mm radian, 5 mm diameter of tumor, and ultra wideband (UWB) bowtie antenna. The models are created and simulated in CST Microwave Studio. For this simulation, we changed antenna to 5 location around the breast, the tumor can be detected when an antenna is close to the tumor location, which the coordinate of maximum SAR is approximated the tumor location. For reliable, we experiment by random tumor location to 3 position in the same size of tumor and simulation the result again by varying the antenna position in 5 position again, and it also detectable the tumor position from the antenna that nearby tumor position by maximum value of SAR, which it can be detected the tumor with precision in all frequency between 4-8 GHz.

Keywords: specific absorption rate (SAR), ultra wideband (UWB), coordinates, cancer detection

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Authors: Tsu-Yin Wu, Jenni Hoffman, Lydia McMurrows, Sarah Lally

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Recent events of the Flint Water Crisis and elevated lead levels in Detroit public school water have highlighted a specific public health disparity and shown the need for better education of healthcare providers on lead education. Identifying children and pregnant women with a high risk for lead poisoning and ensuring lead testing is completed is critical. The purpose of this study is to explore the impact of an educational intervention on knowledge and confidence levels among nursing students enrolled in the prelicensure Bachelor of Science in Nursing (BSN) and Registered Nurse to BSN program (R2B). The study used both quantitative and qualitative research methods to assess the impact of multi-modal pedagogy on knowledge and confidence of lead screening and prevention among prelicensure and R2B nursing students. The students received lead poisoning and prevention content in addition to completing an e-learning module developed by the Pediatric Environmental Health Specialty Units. A total of 115 students completed the pre-and post-test instrument that consisted of demographic, lead knowledge, and confidence items. Despite the increase of total knowledge, three dimensions of lead poisoning, and confidence from pre- to post-test scores for both groups, there was no statistical significance on the increase between prelicensure and R2B students. Thematic analysis of qualitative data showed five themes from participants' learning experiences: lead exposure, signs and symptoms of lead poisoning, screening and diagnosis, prevention, and policy and statewide issues. The study is limited by a small sample and participants recalling some correct answers from the pretest, thus, scoring higher on the post-test. The results contribute to the minimally existent literature examining a critical public health concern regarding lead health exposure and prevention education of nursing students. Incorporating such content area into the nursing curriculum is essential in ensuring that such public health disparities are mitigated.

Keywords: lead poisoning, emerging public health issue, community health, nursing edducation

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Authors: Mostafa Ghasemi, Andrew Urquhart

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In this study, fluorescent carbon dots (CDs) were synthesized in a green way using a one-step hydrothermal method. Carbon dots are carbon-based nanomaterials with a size of less than 10 nm, unique structure, and excellent properties such as low toxicity, good biocompatibility, tunable fluorescence, excellent photostability, and easy functionalization. These properties make them a good candidate to use in different fields such as biological sensing, photocatalysis, photodynamic, and drug delivery. Fourier transformed infrared (FTIR) spectra approved OH/NH groups on the surface of the as-synthesized CDs, and UV-vis spectra showed excellent fluorescence quenching effect of Fe (III) ion on the as-synthesized CDs with high selectivity detection compared with other metal ions. The probe showed a linear response concentration range (0–2.0 mM) to Fe (III) ion, and the limit of detection was calculated to be about 0.50 μM. In addition, CDs also showed good sensitivity to the pH value in the range from 2 to 14, indicating great potential as a pH sensor.

Keywords: carbon dots, fluorescence, pH sensing, metal ions sensor

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Jayant Balaji Athavale, Sean Clarke

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Background: According to the WHO, every year, more than 700,000 people die by suicide, which is one person around every 45 seconds. Suicide is the fourth leading cause of death among 15 to 29-year-olds globally. The most common situations or life events that might cause suicidal thoughts are financial problems/unemployment, rejections, relationship breakups, sexual/substance abuse and mental illnesses. Mental/psychological weakness caused due to defects in one’s personality is one of the main reasons why people feel they cannot cope in such situations and contemplate suicide. A WHO Mental Health Action Plan 2013–2020 lists a 4-point strategy to enhance mental health by ‘implementing strategies for promotion and prevention in mental health.’ Methodology: With 40 years of spiritual research background, the team at the Maharshi University of Spirituality has studied the spiritual root causes that can significantly affect one’s mental health and the solutions to improve it. Results/Findings: According to spiritual science, the time and nature of death are mostly due to spiritual reasons. A person would mostly contemplate and attempt suicide when he is spiritually most vulnerable. Spiritual practice, as per universal principles, helps in protecting a person spiritually and prevents him from getting such thoughts of self-harm or acting upon them by controlling such impulses. The University has had much success in helping people to overcome the defects in their personalities, including those with suicidal thoughts, through spiritual practices such as chanting the Name of God and the Personality Defect Removal (PDR) process developed by the Author. Conclusion: If such techniques were taught in educational institutions, they could be simple yet effective self-help tools to prevent thoughts of suicide and enhance mental health and well-being.

Keywords: suicide, mental health, abuse, suicide prevention, personality defect removal

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Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

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Authors: Padmini S. Navalgund, Manasi Naik, Ujwala Patil

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Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios.

Keywords: Indian road hump, reduced visibility condition, low light condition, adverse light condition, marked hump, unmarked hump, YOLOv9

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Authors: Bienvenu Gael Fouda Mbanga

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This study reports a new approach of preparation of carbon nanoparticles coated cerium oxide nanorods (CNPs/CeONRs) nanocomposite and reusing the spent adsorbent of Cd2+- CNPs/CeONRs nanocomposite for latent fingerprint detection (LFP) after removing Cd2+ ions from aqueous solution. CNPs/CeONRs nanocomposite was prepared by using CNPs and CeONRs with adsorption processes. The prepared nanocomposite was then characterized by using UV-visible spectroscopy (UV-visible), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction pattern (XRD), scanning electron microscope (SEM), Transmission electron microscopy (TEM), Energy-dispersive X-ray spectroscopy (EDS), Zeta potential, X-ray photoelectron spectroscopy (XPS). The average size of the CNPs was 7.84nm. The synthesized CNPs/CeONRs nanocomposite has proven to be a good adsorbent for Cd2+ removal from water with optimum pH 8, dosage 0. 5 g / L. The results were best described by the Langmuir model, which indicated a linear fit (R2 = 0.8539-0.9969). The adsorption capacity of CNPs/CeONRs nanocomposite showed the best removal of Cd2+ ions with qm = (32.28-59.92 mg/g), when compared to previous reports. This adsorption followed pseudo-second order kinetics and intra particle diffusion processes. ∆G and ∆H values indicated spontaneity at high temperature (40oC) and the endothermic nature of the adsorption process. CNPs/CeONRs nanocomposite therefore showed potential as an effective adsorbent. Furthermore, the metal loaded on the adsorbent Cd2+- CNPs/CeONRs has proven to be sensitive and selective for LFP detection on various porous substrates. Hence Cd2+-CNPs/CeONRs nanocomposite can be reused as a good fingerprint labelling agent in LFP detection so as to avoid secondary environmental pollution by disposal of the spent adsorbent.

Keywords: Cd2+-CNPs/CeONRs nanocomposite, cadmium adsorption, isotherm, kinetics, thermodynamics, reusable for latent fingerprint detection

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Authors: Othmane Bouferroum, Malika Boudraa

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In this study, a dual exponential model for CV formant transition is derived from locus theory of speech perception. Then, an algorithm for automatic vowel and consonant’s target formant frequency detection is developed and tested on real speech. The results show that vowels and consonants are detected through transitions rather than their small stable portions. Also, vowel reduction is clearly observed in our data. These results are confirmed by the observations made in perceptual experiments in the literature.

Keywords: acoustic invariance, coarticulation, formant transition, locus equation

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Farheen Tabassum, Shoab Ahmed Khan

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The brutality of attacks on networks and decisive infrastructures are on the climb over recent years and appears to continue to do so. Distributed Denial of service attack is the most prevalent and easy attack on the availability of a service due to the easy availability of large botnet computers at cheap price and the general lack of protection against these attacks. Application layer DDoS attack is DDoS attack that is targeted on wed server, application server or database server. These types of attacks are much more sophisticated and challenging as they get around most conventional network security devices because attack traffic often impersonate normal traffic and cannot be recognized by network layer anomalies. Conventional techniques of single-hosted security systems are becoming gradually less effective in the face of such complicated and synchronized multi-front attacks. In order to protect from such attacks and intrusion, corporation among all network devices is essential. To overcome this issue, a collaborative intrusion detection system (CIDS) is proposed in which multiple network devices share valuable information to identify attacks, as a single device might not be capable to sense any malevolent action on its own. So it helps us to take decision after analyzing the information collected from different sources. This novel attack detection technique helps to detect seemingly benign packets that target the availability of the critical infrastructure, and the proposed solution methodology shall enable the incident response teams to detect and react to DDoS attacks at the earliest stage to ensure that the uptime of the service remain unaffected. Experimental evaluation shows that the proposed collaborative detection approach is much more effective and efficient than the previous approaches.

Keywords: Distributed Denial-of-Service (DDoS), Collaborative Intrusion Detection System (CIDS), Slowloris, OSSIM (Open Source Security Information Management tool), OSSEC HIDS

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Nemi Bhattarai

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In recent years, there has been a rapid increase in the use of Unmanned Aerial Vehicle (UAVs) in search and rescue (SAR) operations, disaster management, and many more areas where information about the location of human beings are important. This research will primarily focus on the use of optical and thermal camera via UAV platform in real-time detection, localization, and visualization of human beings on GIS. This research will be beneficial in disaster management search of lost humans in wilderness or difficult terrain, detecting abnormal human behaviors in border or security tight areas, studying distribution of people at night, counting people density in crowd, manage people flow during evacuation, planning provisions in areas with high human density and many more.

Keywords: UAV, human detection, real-time, localization, visualization, haar-like, GIS, thermal sensor

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Dinesh Kumar Sharma, Sanjay Sharma

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Textile manufacturing industry uses a substantial amount of chemicals not only in the production processes but also in manufacturing the raw materials. Dyes are the most significant raw material which provides colour to the fabric and yarn. Dyes are produced by using a large amount of chemicals both organic and inorganic in nature. Dyes are further classified as Reactive or Vat Dyes which are mostly used in cotton textiles. In the process of application of dyes to the cotton fiber, yarn or fabric, several auxiliary chemicals are also used in the solution called dyebath to improve the absorption of dyes. There is a very little absorption of dyes and auxiliary chemicals and a residual amount of all these substances is released as the spent dye bath effluent. Because of the wide variety of chemicals used in cotton textile dyes, there is always a risk of harmful effects which may not be apparent immediately but may have an irreversible impact in the long term. Colour imparted by the dyes to the water also has an adverse effect on its public acceptability and the potability. This study has been conducted with an objective to assess the feasibility of reuse of the spent dye bath. Studies have been conducted in two independent industries manufacturing dyed cotton yarn and dyed cotton fabric respectively. These have been referred as Unit-I and Unit-II. The studies included assessment of reduction in pollution levels and the economic benefits of such reuse. The study conclusively establishes that the reuse of spent dyebath results in prevention of pollution, reduction in pollution loads and cost of effluent treatment & production. This pollution prevention technique presents a good preposition for pollution prevention in cotton textile industry.

Keywords: dyes, dyebath, reuse, toxic, pollution, costs

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: James Kaweesa

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The Internet of Things (IoT) has become a critical component of modern technology, enabling the connection of numerous devices to the internet. The interconnected nature of IoT devices, along with their heterogeneous and resource-constrained nature, makes them vulnerable to various types of attacks, such as malware, denial-of-service attacks, and network scanning. Intrusion Detection Systems (IDSs) are a key mechanism for protecting IoT networks and from attacks by identifying and alerting administrators to suspicious activities. In this review, the paper will discuss the different types of IDSs available for IoT systems and evaluate their effectiveness in detecting and preventing attacks. Also, examine the various evaluation methods used to assess the performance of IDSs and the challenges associated with evaluating them in IoT environments. The review will highlight the need for effective and efficient IDSs that can cope with the unique characteristics of IoT networks, including their heterogeneity, dynamic topology, and resource constraints. The paper will conclude by indicating where further research is needed to develop IDSs that can address these challenges and effectively protect IoT systems from cyber threats.

Keywords: cyber-threats, iot, intrusion detection system, networks

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Authors: Desissa Yadeta Muleta

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Purely organic long-persistent luminescence materials (OLPLMs) have been developed as emerging organic materials due to their simple production process, low preparation cost and better biocompatibilities. Notably, OLPLMs with afterglow-time-tunable long-persistent luminescence (LPL) characteristics enable higher-level protection applications and have great prospects in biological applications. The realization of these advanced performances depends on our ability to gradually tune LPL duration under ambient conditions, however, the strategies to achieve this are few due to the lack of unambiguous mechanisms. Here, we propose a two-step strategy to gradually tune LPL duration of OLPLMs over a wide range of seconds in water and air, by using derivatives as the guest and introducing a third-party material into the host-immobilized host–guest doping system. Based on this strategy, we develop an analysis method for deoxyribonucleic acid (DNA) content detection without DNA separation in aqueous samples, which circumvents the influence of the chromophore, fluorophore and other interferents in vivo, enabling a certain degree of in situ detection that is difficult to achieve using today’s methods. This work will expedite the development of afterglow-time-tunable OLPLMs and expand new horizons for their applications in data protection, bio-detection, and bio-sensing

Keywords: deoxyribonucliec acid, long persistent luminescent materials, water, air

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: H. Abu-Ali, A. Nabok, T. Smith

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Aptamers are single-strand of DNA or RNA nucleotides sequence which is designed in vitro using selection process known as SELEX (systematic evolution of ligands by exponential enrichment) were developed for the selective detection of many toxic materials. In this work, we have developed an electrochemical biosensor for highly selective and sensitive detection of Hg2+ and Pb2+ using a specific aptamer probe (SAP) labelled with ferrocene (or methylene blue) in (5′) end and the thiol group at its (3′) termini, respectively. The SAP has a specific coil structure that matching with G-G for Pb2+ and T-T for Hg2+ interaction binding nucleotides ions, respectively. Aptamers were immobilized onto surface of screen-printed gold electrodes via SH groups; then the cyclic voltammograms were recorded in binding buffer with the addition of the above metal salts in different concentrations. The resulted values of anode current increase upon binding heavy metal ions to aptamers and analyte due to the presence of electrochemically active probe, i.e. ferrocene or methylene blue group. The correlation between the anodic current values and the concentrations of Hg2+ and Pb2+ ions has been established in this work. To the best of our knowledge, this is the first example of using a specific DNA aptamers for electrochemical detection of heavy metals. Each increase in concentration of 0.1 μM results in an increase in the anode current value by simple DC electrochemical test i.e (Cyclic Voltammetry), thus providing an easy way of determining Hg2+ and Pb2+concentration.

Keywords: aptamer, based, biosensor, DNA, electrochemical, highly, specific

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: McClain Thiel

Abstract:

Object detection and localization has historically required two or more sensors due to the loss of information from 3D to 2D space, however, most surveillance systems currently in use in the real world only have one sensor per location. Generally, this consists of a single low-resolution camera positioned above the area under observation (mall, jewelry store, traffic camera). This is not sufficient for robust 3D tracking for applications such as security or more recent relevance, contract tracing. This paper proposes a lightweight system for 3D person tracking that requires no additional hardware, based on compressed object detection convolutional-nets, facial landmark detection, and projective geometry. This approach involves classifying the target into a demographic category and then making assumptions about the relative locations of facial landmarks from the demographic information, and from there using simple projective geometry and known constants to find the target's location in 3D space. Preliminary testing, although severely lacking, suggests reasonable success in 3D tracking under ideal conditions.

Keywords: monocular distancing, computer vision, facial analysis, 3D localization

Procedia PDF Downloads 139

Authors: Haydar Teymourlouei

Abstract:

It is important to take security measures to protect your computer information, reduce identify theft, and prevent from malicious cyber-attacks. With cyber-attacks on the continuous rise, people need to understand and learn ways to prevent from these attacks. Cyber-attack is an important factor to be considered if one is to be able to protect oneself from malicious attacks. Without proper security measures, most computer technology would hinder home users more than such technologies would help. Knowledge of how cyber-attacks operate and protective steps that can be taken to reduce chances of its occurrence are key to increasing these security measures. The purpose of this paper is to inform home users on the importance of identifying and taking preventive steps to avoid cyberattacks. Throughout this paper, many aspects of cyber-attacks will be discuss: what a cyber-attack is, the affects of cyber-attack for home users, different types of cyber-attacks, methodology to prevent such attacks; home users can take to fortify security of their computer.

Keywords: cyber-attacks, home user, prevention, security, technology

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Authors: M. A. Alavianmehr, A. Tashk, A. Sodagaran

Abstract:

Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method.

Keywords: image processing, background models, video surveillance, foreground detection, Gaussian mixture model

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Authors: Eric Ng, Freddy W. Wagey, Frank M. M. Wagey

Abstract:

Background: Cervical cancer is the fourth most common cancer in women worldwide and the most common cancer in many low- and middle-income countries. The main causes are the lack of prevention programs and effective therapy, as well as the lack of knowledge about cervical cancer and awareness for early detection. The Pap smear test and visual inspection with acetic acid (VIA) allow the cervical lesion to be detected so that progression to cervical cancer can be avoided. Objective: The purpose of this study was to evaluate the knowledge of Pap smear test and VIA in cervical cancer patients. Methodology: A total of 67 cervical cancer patients in Manado who volunteered to participate in the research were identified as the sample. The data were collected during the month of November 2019-January 2020 with a questionnaire about the respondents' knowledge relating to Pap smear test and VIA. Questionnaire data were analysed using descriptive statistics. Results: Knowledge of pap smear among cervical cancer patients were good in 9 respondents (13.4%), moderate in 20 respondents (29.9%), and bad in 38 respondents (56.7%), whereas the knowledge of VIA was good in 13 respondents (19.4%), moderate in 15 respondents (22.4%), and bad in 39 respondents (58.2%). Conclusion: Majority of cervical cancer patients in Manado still had bad knowledge about Pap smear tests and VIA.

Keywords: cervical cancer, knowledge, pap smear test, visual inspection with acetic acid

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Authors: Ashish Kumar, Kaptan Singh, Amit Saxena

Abstract:

Today, it is believed that crimes have the greatest impact on a person's ability to progress financially and personally. Identifying places where individuals shouldn't go is crucial for preventing crimes and is one of the key considerations. As society and technologies have advanced significantly, so have crimes and the harm they wreak. When there is a concentration of people in one place and changes happen quickly, it is even harder to prevent. Because of this, many crime prevention strategies have been embraced as a component of the development of smart cities in numerous cities. However, crimes can occur anywhere; all that is required is to identify the pattern of their occurrences, which will help to lower the crime rate. In this paper, an analysis related to crime has been done; information related to crimes is collected from all over India that can be accessed from anywhere. The purpose of this paper is to investigate the relationship between several factors and India's crime rate. The review has covered information related to every state of India and their associated regions of the period going in between 2001- 2014. However various classes of violations have a marginally unique scope over the years.

Keywords: K-nearest neighbor, random forest, decision tree, pre-processing

Procedia PDF Downloads 92

Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

Abstract:

Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

Procedia PDF Downloads 130