Search results for: human detection

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: Adegboyega Adeolapo Ola, Gerelene Jagganath

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One of the greatest challenges of human right in the world, especially African states is the use of child soldiers in armed conflict, constituting a major source of destruction of lives and properties. Mostly, they are in developing countries with the situation in Sub-Saharan Africa, the abduction and employment of children as soldiers is a form of exploitative labour that is tantamount to slavery. Since the end of cold war, Child soldier has increased in Africa countries like Angola, Liberia, Sierra Leone and Uganda. This study examines the main cause of the recruitment and use of child soldiers and its challenges to human right. It further assesses the role of international regional bodies and various governments in curbing child soldiers with a view to proffer suggestions on how to address some of the resultant threat of human right. The study posits that the control of small arms and light weapons is essential in curtailing the spread of child soldier and abuse of human right. This hopefully should result in the sustainability of human/child right in African continent. It is a recommendation of this study that, in order to sustain human right in the region, all Africa leaders, government and regional bodies; such as African Union, Economic Community of West African States, South African Development Community among others, should cooperate and work together to address the issue of illicit small arms, which could eventually lead to child soldier.

Keywords: arms control, child soldier, human right, small arms

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Authors: Sikhumbuzo Zondi, Serges A. Kamga

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Human security and human rights are mutually reinforcing concepts given that human security addresses questions related to human conditions such as the safety of individuals and the protection of individual rights and civil liberties. It does this by suggesting that the proper referent for security should be the individual and not the nation-state, due to the individual’s vulnerability to threats such as malnutrition and poverty, conflicts, exploitation and marginalization, despotism and climate change. Due to the primacy of the individual, human security comfortably expand to the notion of social justice, given that for far too-long, many individuals around the world have been denied of their basic human rights through racial discrimination, unfair labour and segregation policies and as a result encountered widespread social, environmental and economic injustices which are evident in the current structural division of the world between the developed north and the underdeveloped or developing south. In light of this view, ensuring freedom from want and freedom from fear, for all individuals is arguably the sound route to addressing and remedying the global ills of our time and a way to promoting human rights for all. The promotion of human security provides an important part of human/societal progress because inclusive security facilitates development and human rights protection, while insecurity reduces people’s growth and investment prospects and prolongs historical injustices. Therefore, this paper seeks to show that human security and human rights complements one another and that this correlation provides the necessary mechanisms for addressing and remedying the historical injustices that still affect most of the world’s population. It will look at linkages between human security and the individual right to equality and freedom from discrimination, right to life, liberty, and personal security; development; own property; adequate living standard; education; desirable work and to join trade unions; participate in government and in free elections; social security and equality before the law. The paper considers these human rights and liberties as vital for securing the core values of human life while at the same addressing socio-economic injustices that still persist in the contemporary world. The paper will be a desktop study using qualitative research methods on two case studies in Africa namely Cameroun and South Africa.

Keywords: justice, human security, human rights, injustices

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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

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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

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

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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: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

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

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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

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Authors: Md. Abdul Aziz

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Sulfide ion is water soluble, highly corrosive, toxic and harmful to the human beings. As a result, knowing the exact concentration of sulfide in water is very important. However, the existing detection and quantification methods have several shortcomings, such as high cost, low sensitivity, and massive instrumentation. Consequently, the development of novel sulfide sensor is relevant. Nevertheless, electrochemical methods gained enormous popularity due to a vast improvement in the technique and instrumentation, portability, low cost, rapid analysis and simplicity of design. Successful field application of electrochemical devices still requires vast improvement, which depends on the physical, chemical and electrochemical aspects of the working electrode. The working electrode made of bulk gold (Au) and platinum (Pt) are quite common, being very robust and endowed with good electrocatalytic properties. High cost, and electrode poisoning, however, have so far hindered their practical application in many industries. To overcome these obstacles, we developed a sulfide sensor based on an indium tin oxide nanoparticle (ITONP)-modified ITO electrode. To prepare ITONP-modified ITO, various methods were tested. Drop-drying of ITONPs (aq.) on aminopropyltrimethoxysilane-functionalized ITO (APTMS/ITO) was found to be the best method on the basis of voltammetric analysis of the sulfide ion. ITONP-modified APTMS/ITO (ITONP/APTMS/ITO) yielded much better electrocatalytic properties toward sulfide electro-οxidation than did bare or APTMS/ITO electrodes. The ITONPs and ITONP-modified ITO were also characterized using transmission electron microscopy and field emission scanning electron microscopy, respectively. Optimization of the type of inert electrolyte and pH yielded an ITONP/APTMS/ITO detector whose amperometrically and chronocoulοmetrically determined limits of detection for sulfide in aqueous solution were 3.0 µM and 0.90 µM, respectively. ITONP/APTMS/ITO electrodes which displayed reproducible performances were highly stable and were not susceptible to interference by common contaminants. Thus, the developed electrode can be considered as a promising tool for sensing sulfide.

Keywords: amperometry, chronocoulometry, electrocatalytic properties, ITO-nanoparticle-modified ITO, sulfide sensor

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Ploychompoo Kittikunchotiwut

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This study examines the relationships between human resource practices, knowledge management capacity, and innovation performance. The data were collected by using a questionnaire from 241 firms in the hotels in Thailand. The hypothesized relationships among variables are examined by using ordinary least square (OLS) regression analysis. The findings show that human resource practices have a positive effect on knowledge management capacity. Besides, knowledge management capacity was found to positively affect innovation performance. Finally, the limitations of the study and directions for future research are discussed.

Keywords: human resource practices, knowledge management capacity, innovation performance

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Authors: Amin Osama Amin Mohamed Elbaramawy

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In the last period of time, about ten years ago, wars caused violations of human rights in many places, and despite international condemnations, they did not stop, and the truth is that international law was unable to stop them. The global wars and conflicts that the world has been witnessing for more than ten years have caused the displacement of millions of people in all parts of the earth, causing a violation of the human rights of those people. Despite international condemnations of these conflicts, these conflicts have not stopped and have not been resolved until now. Therefore, I call for international law and international courts to be more effective and not just in words, taking into account the speed in this due to the increase in those wars and conflicts every day and new violations every day.

Keywords: war, freedom, human rights, international law

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Christoph Opperer

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Space syntax is a theory and method for analyzing the spatial layout of buildings and urban environments to understand how they can influence patterns of human movement, social interaction, and behavior. While direct visibility is a key factor in space syntax research, important visual information such as light, color, texture, etc., are typically not considered, even though psychological studies have shown a strong correlation to the human perceptual experience within physical space – with light and color, for example, playing a crucial role in shaping the perception of spaciousness. Furthermore, these surface properties are often the visual features that are most salient and responsible for drawing attention to certain elements within the environment. This paper explores the potential of integrating these factors into general space syntax methods and visibility-based analysis of space, particularly for architectural spatial layouts. To this end, we use a combination of geometric (isovist) and topological (visibility graph) approaches together with image-based methods, allowing a comprehensive exploration of the relationship between spatial geometry, visual aesthetics, and human experience. Custom-coded ray-tracing techniques are employed to generate spherical panorama images, encoding three-dimensional spatial data in the form of two-dimensional images. These images are then processed through computer vision algorithms to generate saliency-maps, which serve as a visual representation of areas most likely to attract human attention based on their visual properties. The maps are subsequently used to weight the vertices of isovists and the visibility graph, placing greater emphasis on areas with high saliency. Compared to traditional methods, our weighted visibility analysis introduces an additional layer of information density by assigning different weights or importance levels to various aspects within the field of view. This extends general space syntax measures to provide a more nuanced understanding of visibility patterns that better reflect the dynamics of human attention and perception. Furthermore, by drawing parallels to traditional isovist and VGA analysis, our weighted approach emphasizes a crucial distinction, which has been pointed out by Ervin and Steinitz: the difference between what is possible to see and what is likely to be seen. Therefore, this paper emphasizes the importance of including surface properties in visibility-based analysis to gain deeper insights into how people interact with their surroundings and to establish a stronger connection with human attention and perception.

Keywords: space syntax, visibility analysis, isovist, visibility graph, visual features, human perception, saliency detection, raytracing, spherical images

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Authors: Ilana Margalith, Adaya Meirowitz, Sigalit Cohavi

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Violence against health professionals by patients and their families have recently become a disturbing phenomenon worldwide, exacting psychological as well as economic tolls. Health workplaces in Israel (e.g. hospitals and H.M.O clinics) provide workshops for their employees, supplying them with coping strategies. However, these workshops do not focus on nursing students, who are also subjected to this violence. Their learning environment is no longer as protective as it used to be. Furthermore, coping with violence was not part of the curriculum for Israeli nursing students. Thus, based on human aggression theories which depict the pivotal role of the professional's correct response in preventing the onset of an aggressive response or the escalation of violence, a workshop was developed for undergraduate nursing students at the Clalit Nursing Academy, Rabin Campus (Dina), Israel. The workshop aimed at reducing students' anxiety vis a vis the aggressive patient or family in addition to strengthening their ability to cope with such situations. The students practiced interpersonal skills, especially relevant to early detection of potential violence, as well as ‘a correct response’ reaction to the violence, thus developing the necessary steps to be implemented when encountering violence in the workplace. In order to assess the efficiency of the workshop, the participants filled out a questionnaire comprising knowledge and self-efficacy scales. Moreover, the replies of the 23 participants in this workshop were compared with those of 24 students who attended a standard course on interpersonal communication. Students' self-efficacy and knowledge were measured in both groups before and after the course. A statistically significant interaction was found between group (workshop/standard course) and time (before/after) as to the influence on students' self-efficacy (p=0.004) and knowledge (p=0.007). Nursing students, who participated in this ‘coping with workplace violence’ workshop, gained knowledge, confidence and a sense of self-efficacy with regard to workplace violence. Early detection of signs of imminent violence amongst patients or families and the prevention of its escalation, as well as the ability to manage the threatening situation when occurring, are acquired skills. Encouraging nursing students to learn and practice these skills may enhance their ability to cope with these unfortunate occurrences.

Keywords: early detection of violence, nursing students, patient aggression, self-efficacy, workplace violence

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Authors: Elmahdi Khalil, Saad Enniari, Mostapha Zbakh

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Cloud computing promises to increase innovation and the velocity with witch applications are deployed, all while helping any enterprise meet most IT service needs at a lower total cost of ownership and higher return investment. As the march of cloud continues, it brings both new opportunities and new security challenges. To take advantages of those opportunities while minimizing risks, we think that Intrusion Detection Systems (IDS) integrated in the cloud is one of the best existing solutions nowadays in the field. The concept of intrusion detection was known since past and was first proposed by a well-known researcher named Anderson in 1980's. Since that time IDS's are evolving. Although, several efforts has been made in the area of Intrusion Detection systems for cloud computing environment, many attacks still prevail. Therefore, the work presented in this paper proposes a multi criteria analysis and a comparative study between several IDS architectures designated to work in a cloud computing environments. To achieve this objective, in the first place we will search in the state of the art of several consistent IDS architectures designed to work in a cloud environment. Whereas, in a second step we will establish the criteria that will be useful for the evaluation of architectures. Later, using the approach of multi criteria decision analysis Mac Beth (Measuring Attractiveness by a Categorical Based Evaluation Technique we will evaluate the criteria and assign to each one the appropriate weight according to their importance in the field of IDS architectures in cloud computing. The last step is to evaluate architectures against the criteria and collecting results of the model constructed in the previous steps.

Keywords: cloud computing, cloud security, intrusion detection/prevention system, multi-criteria decision analysis

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Authors: Firdaus Khairi Abdul Kadir, Nazihah Rusli, Noor Aisyah Abdul Aziz

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Islam is a comprehensive religion encompassing all aspects of society’s life such as social, economic, political, cultural and environmental. The environment is part of the manifestation of God’s greatness which has pearls of wisdom, bestowed upon human beings to make them realize that everything is in the hands of God (Allah SWT). However, the equilibrium of nature could be disturbed from the excessive exploitation by humans’ hands. As a caliph on this earth, it is the responsibility of human beings to look after the environment proactively. Besides, Islam calls for the execution of accountable development and respecting the principles of sustainability. Therefore, this study focuses on the role of human beings as caliphs on this earth who are responsible for nature and their acts in conserving and preserving the environment based on the approach of religious education. This study also used the research method of the survey library.

Keywords: environment, human beings, caliph, tauhid, Allah SWT

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Authors: Nusrat Praween, Krishna Thej Pammi Guru, Palash Kumar Basu

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Designing non-invasive biosensors for cancer diagnosis is essential for developing an affordable and specific tool to measure cancer-related exosome biomarkers. Exosomes, released by healthy as well as cancer cells, contain valuable information about the biomarkers of various diseases, including cancer. Despite the availability of various isolation techniques, ultracentrifugation is the standard technique that is being employed. Post isolation, exosomes are traditionally exposed to detergents for extracting their proteins, which can often lead to protein degradation. Further to this, it is very essential to develop a sensing platform for the quantification of clinically relevant proteins in a wider range to ensure practicality. In this study, exosomes were immobilized on the Au Screen Printed Electrode (SPE) using EDC/NHS chemistry to facilitate binding. After immobilizing the exosomes on the screen-printed electrode (SPE), we investigated the impact of the electric field by applying various voltages to induce exosome lysis and release their contents. The lysed solution was used for sensing TSG101, a crucial biomarker associated with various cancers, using both faradaic and non-faradaic electrochemical impedance spectroscopy (EIS) methods. The results of non-faradaic and faradaic EIS were comparable and showed good consistency, indicating that non-faradaic sensing can be a reliable alternative. Hence, the non-faradaic sensing technique was used for label-free quantification of the TSG101 biomarker. The results were validated using ELISA. Our electrochemical immunosensor demonstrated a consistent response of TSG101 from 125 pg/mL to 8000 pg/mL, with a detection limit of 0.125 pg/mL at room temperature. Additionally, since non-faradic sensing is label-free, the ease of usage and cost of the final sensor developed can be reduced. The proposed immunosensor is capable of detecting the TSG101 protein at low levels in healthy serum with good sensitivity and specificity, making it a promising platform for biomarker detection.

Keywords: biosensor, exosomes isolation on SPE, electric field lysis of exosome, EIS sensing of TSG101

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: Yang Li, Xiaorong Guan, Cheng Xu

Abstract:

In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload

Procedia PDF Downloads 348

Authors: Kelemewerk Destalem, Joongjae Cho, Jaeseong Lee, Ju H. Park, Joonhyuk Yoo

Abstract:

Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method.

Keywords: background subtraction, background updating, real time, light weight algorithm, temporal difference

Procedia PDF Downloads 322

Authors: Mehwish Asghar

Abstract:

Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

Procedia PDF Downloads 203

Authors: K. Frahtia, I. Mihoubi, S. Picot

Abstract:

Leishmaniasis is a group of parasitic disease with a varied clinical expression caused by flagellate protozoa of the Leishmania genus. These diseases are transmitted to humans and animals by the sting of a vector insect, the female sandfly. Among the groups of dipteral disease vectors, Phlebotominae occupy a prime position and play a significant role in human pathology, such as leishmaniasis that affects nearly 350 million people worldwide. The vector control operation launched by health services throughout the country proves to be effective since despite the prevalence of the disease remains high especially in rural areas, leishmaniasis appears to be declining in Algeria. In this context, this study mainly concerns molecular detection of Leishmania from the vector. Furthermore, a molecular diagnosis has also been made on skin samples taken from patients in the region of Constantine, located in the North-East of Algeria. Concerning the vector, 5858 sandflies were captured, including 4360 males and 1498 females. Male specimens were identified based on their morphological. The morphological identification highlighted the presence of the Phlebotomus genus with a prevalence of 93% against 7% represented by the Sergentomyia genus. About the identified species, P. perniciosus is the most abundant with 59.4% of the male identified population followed by P. longicuspis with 24.7% of the workforce. P. perfiliewi is poorly represented by 6.7% of specimens followed by P. papatasi with 2.2% and 1.5% S. dreyfussi. Concerning skin samples, 45/79 (56.96%) collected samples were found positive by real-time PCR. This rate appears to be in sharp decline compared to previous years (alert peak of 30,227 cases in 2005). Concerning the detection of Leishmania from sandflies by RT-PCR, the results show that 3/60 PCR performed genus are positive with melting temperatures corresponding to that of the reference strain (84.1 +/- 0.4 ° C for L. infantum). This proves that the vectors were parasitized. On the other side, identification by RT-PCR species did not give any results. This could be explained by the presence of an insufficient amount of leishmanian DNA in the vector, and therefore support the hypothesis of the regression of leishmaniasis in Constantine.

Keywords: Algeria, molecular diagnostic, phlebotomus, real time PCR

Procedia PDF Downloads 258

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

Abstract:

In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 97

Authors: A. Karasek

Abstract:

Rapid growth of Information Technologies (IT) has had huge influence on enterprises, and it has contributed to its promotion and increasingly extensive use in enterprises. Information Technologies have to a large extent determined the processes taking place in a enterprise; what is more, IT development has brought the need to adopt a brand new approach to human resources management in an enterprise. The use of IT in Human Resource Management (HRM) is of high importance due to the growing role of information and information technologies. The aim of this paper is to evaluate the use of information technologies in human resources management in enterprises. These practices will be presented in the following areas: Recruitment and selection, development and training, employee assessment, motivation, talent management, personnel service. Results of conducted survey show diversity of solutions applied in particular areas of human resource management. In the future, further development in this area should be expected, as well as integration of individual HRM areas, growing mobile-enabled HR processes and their transfer into the cloud. Presented IT solutions applied in HRM are highly innovative, which is of great significance due to their possible implementation in other enterprises.

Keywords: e-HR, human resources management, HRM practices, HRMS, information technologies

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Authors: Sheetla Prasad

Abstract:

The objective of this study is ‘discovering the real psyche of human beings for prediction of mode, direction and strength of the potential of actions of the individual. The human face was taken as a source of central point to search for the route of real psyche. Analysis of the face architecture (shape and salient features of face) was done by three directional photographs ( 600 left and right and camera facing) of human beings. The shapes and features of the human face were scaled in 177 units on the basis of face–features locations (FFL). The mathematical analysis was done of FFLs by self developed and standardized formula. At this phase, 800 samples were taken from the population of students, teachers, advocates, administrative officers, and common persons. The finding shows that real psyche has two external rings (ER). These ER are itself generator of two independent psyches (manifested and manipulated). Prima-facie, it was proved that micro differences in FFLs have potential to predict the state of art of the human psyche. The potential of psyches was determined by the saving and distribution of mental energy. It was also mathematically proved.

Keywords: face architecture, psyche, potential, face functional ratio, external rings

Procedia PDF Downloads 490