Search results for: Credit Card Fraud Detection

Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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Authors: Meet Shah, Ankita Aditya, Dhruv Bindra, V. S. Omkar, Aashruti Seervi

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In today's world, we need documents everywhere for a smooth workflow in the identification process or any other security aspects. The current system and techniques which are used for identification need one thing, that is ‘proof of existence’, which involves valid documents, for example, educational, financial, etc. The main issue with the current identity access management system and digital identification process is that the system is centralized in their network, which makes it inefficient. The paper presents the system which resolves all these cited issues. It is based on ‘blockchain’ technology, which is a 'decentralized system'. It allows transactions in a decentralized and immutable manner. The primary notion of the model is to ‘have everything with nothing’. It involves inter-linking required documents of a person with a single identity card so that a person can go anywhere without having the required documents with him/her. The person just needs to be physically present at a place wherein documents are necessary, and using a fingerprint impression and an iris scan print, the rest of the verification will progress. Furthermore, some technical overheads and advancements are listed. This paper also aims to layout its far-vision scenario of blockchain and its impact on future trends.

Keywords: blockchain, decentralized system, fingerprint impression, identity management, iris scan

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohammad Sharef

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Sarcasm is a common phenomenon in social media which is a nuanced form of language for stating the opposite of what is implied. Due to the intentional ambiguity, analysis of sarcasm is a difficult task not only for a machine but even for a human. Although sarcasm detection has an important effect on sentiment, it is usually ignored in social media analysis because sarcasm analysis is too complicated. While there is a few systems exist which can detect sarcasm, almost no work has been carried out on a study and the review of the existing work in this area. This survey presents a nearly full image of sarcasm detection techniques and the related fields with brief details. The main contributions of this paper include the illustration of the recent trend of research in the sarcasm analysis and we highlight the gaps and propose a new framework that can be explored.

Keywords: sarcasm detection, sentiment analysis, social media, sarcasm analysis

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Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Zhizhong Yuan, Lili Wang, Chenya Zheng, Wuqi Yang

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How to encourage and support peer-to-peer (P2P) network lending, and effectively monitor the risk of P2P network lending, has become the focus of the Chinese government departments, industrialists, experts and scholars in recent years. The reason is that this convenient online micro-credit service brings a series of credit risks and other issues. Avoiding the risks brought by the P2P network lending model, it can better play a benign role and help China's small and medium-sized private enterprises with vigorous development to solve the capital needs; otherwise, it will bring confusion to the normal financial order. As a form of financial services, P2P network lending has injected new blood into China's non-government finance in the past ten years, and has found a way out for idle funds and made up for the shortage of traditional financial services in China. However, it lacks feasible measures in credit evaluation and government supervision. This paper collects a large amount of data about P2P network lending of China. The data collection comes from the official media of the Chinese government, the public achievements of existing researchers and the analysis and collation of correlation data by the authors. The research content of this paper includes literature review; the current situation of China's P2P network lending development; the risk analysis of P2P network lending in China; the risk prevention strategy of P2P network lending in China. The focus of this paper is to try to find a specific program to strengthen supervision and avoid risks from the perspective of government regulators, operators of P2P network lending platform, investors and users of funds. These main measures include: China needs to develop self-discipline organization of P2P network lending industry and formulate self-discipline norms as soon as possible; establish a regular information disclosure system of P2P network lending platform; establish censorship of credit rating of borrowers; rectify the P2P network lending platform in compliance through the implementation of bank deposition. The results and solutions will benefit all the P2P network lending platforms, creditors, debtors, bankers, independent auditors and government agencies of China and other countries.

Keywords: peer-to-peer(P2P), regulation, risk prevention, supervision

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Authors: Zakheleni P. Dube, Phatu W. Mashela

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The Curve-fitting Allelochemical Response Data (CARD) model had been adopted as a valuable tool in enhancing the understanding of the efficacy of cucurbitacin-containing phytonematicides on the suppression of nematodes. In most cases, for registration purposes, the active ingredients should be in purified form. Evidence in other phytonematicides suggested that purified active ingredients were less effective in suppression of nematodes. The objective of this study was to use CARD model to compare the overall sensitivities of Meloidogyne incognita J2 hatch, mobility and mortality to Nemarioc-AL phytonematicides, cucurbitacin A, Nemafric-BL phytonematicide and cucurbitacin B. Meloidogyne incognita eggs and J2 were exposed to 0.00, 0.50, 1.00, 1.50, 2.00, 2.50, 3.00, 3.50, 4.00, 4.50 and 5.00% of each phytonematicide, whereas in purified form the concentrations were 0.00, 0.25, 0.50, 0.75, 1.00, 1.25, 1.50, 1.75, 2.00, 2.25 and 2.50 μg.mL⁻¹. The exposure period to each concentration was 24-, 48- and 72-h. The overall sensitivities of J2 hatch to Nemarioc-AL phytonematicide, cucurbitacin A, Nemafric-BL phytonematicide and cucurbitacin B were 1, 30, 5 and 2 units, respectively, whereas J2 mobiltity were 3, 17, 3 and 6 units, respectively. For J2 mortality overall sensitivities to Nemarioc-AL phytonematicide, cucurbitacin A, Nemafric-BL phytonematicide and cucurbitacin B were 2, 4, 1 and 4 units, respectively. In conclusion, the two crude extracts, Nemarioc-AL and Nemafric-BL phytonematicides were generally more potent to M. incognita compared to their pure active ingredients. The crude plant extract preparation is easy, and they could be an ideal tactic for the management of nematodes in resource poor farming communities.

Keywords: Botanicals, cucumin, leptodermin, plant extracts, triterpenoids

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Authors: Hao Cheng, Zhiwu Wang, Guozheng Yan, Pingping Jiang, Shijia Qin, Shuai Kuang

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Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.

Keywords: AFI, edge detection, adaptive threshold, morphological processing, OV6930, FPGA

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Authors: Juneseok You, Chanho Park, Kuehwan Jang, Sungsoo Na

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Sensing of nanotoxic materials is strongly important, as their engineering applications are growing recently and results in that nanotoxic material can harmfully influence human health and environment. In current study we report the quartz crystal microbalance (QCM)-based, in situ and real-time sensing of nanotoxic-material by frequency shift. We propose the in situ detection of nanotoxic material of zinc oxice by using QCM functionalized with a taget-specific DNA. Since the mass of a target material is comparable to that of an atom, the mass change caused by target binding to DNA on the quartz electrode is so small that it is practically difficult to detect the ions at low concentrations. In our study, we have demonstrated the in-situ and fast detection of zinc oxide using the quartz crystal microbalance (QCM). The detection was derived from the DNA hybridization between the DNA on the quartz electrode. The results suggest that QCM-based detection opens a new avenue for the development of a practical water-testing sensor.

Keywords: nanotoxic material, qcm, frequency, in situ sensing

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Authors: Magdalena Barbara Kaziuk, Ilona Hubner, Jacek Hubner, Slawomir Kroczka

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Background: The motor neuron disease is a progressive neurodegenerative disease causing malfunction. Irrespective of the form of the disease and its onset always leads to the worsening of the quality of life, with patients usually depending on the family. Materials and methods: The study included 20 persons (5 females, 15 males, aged 65,5 ± 20 years) with clinically certain or probable diagnosis of the motor neuron disease. Patients were examined three times in the period of six months. The diagnosis was established based on the criteria of El Escorial. Manual dexterity was assessed using the test of the card Rene Zazzo and the test of shading in with lines Mira Stambak. Results: All patients achieved unsatisfactory results in Rene Zazzo’s test of the card and most of the patients (60%) in Mira Stambak’s test of shading with lines. Significantly higher test results were achieved for Rene Zazzo’s test and lower test results for Mira Stambak’s test in consecutive measurements. Conclusions: Impairment of manual dexterity is present already at the moment of diagnosing the disease and is growing significantly during its course. The quality of life for MND patients undergoes gradual deterioration as a result of the malfunction.

Keywords: manual dexterity, motor neuron disease, quality of life, malfunction

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Authors: Hamid Boustanifar, Geraldo Cerqueiro, María Fabiana Penas

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We exploit state-level changes in the amount of personal wealth individuals can protect under Chapter 7 to analyze the causal effect of debtor protection on income inequality. We find that an increase in state exemptions significantly increases inequality by reducing income for low-income individuals and by increasing income for high-income individuals. The increase in inequality is four times larger among the self-employed than among wage earners, and it is due mainly to a growing income gap between skilled (i.e., individuals with a college degree) and unskilled entrepreneurs. We also find that the employment rate of skilled entrepreneurs significantly increases, while the employment rate of unskilled wage earners falls. Our results are consistent with a recent literature that shows that higher exemptions redistribute credit from low-wealth to high-wealth entrepreneurs, affecting the performance of their businesses.

Keywords: debtor protection, credit markets, income inequality, debtor protection laws

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Authors: Muhammad Muhammad Suleiman

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The Nigerian political system has witnessed rising occurrences of election malpractice in the last decade. This has been due to election rigging and other forms of electoral fraud. In order to find a sustainable solution to this malpractice, the introduction of electronic voting (e-voting) has been suggested. This paper reviews the challenges, benefits, and issues associated with e-voting as a panacea for election malpractice in Nigeria. The review of existing literature revealed that e-voting can reduce the cost of conducting elections and reduce the opportunity for electoral fraud. The review suggests that the introduction of e-voting in the Nigerian political system would require adequate cybersecurity measures, trust-building initiatives, and proper legal frameworks to ensure its successful implementation. It is recommended that there should be an effective policy that would ensure the security of the system as well as the credibility of the results. Furthermore, a comprehensive awareness campaign needs to be conducted to ensure that voters understand the process and are comfortable using the system. In conclusion, e-voting has the potential to reduce the occurrence of election malpractice in the Nigerian political system. However, the successful implementation of e-voting will require effective policy interventions and trust-building initiatives. Additionally, the costs of acquiring the necessary infrastructure and equipment and implementing proper legal frameworks need to be considered.

Keywords: electronic voting, general election, candidate, INEC, cyberattack

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Authors: Oluwole Opeyemi Dina, Saheed E. Elugoke, Peter Olutope Fayemi, Omolola E. Fayemi

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This study presents the detection of epinephrine (EP) at Fe₃O₄ modified screen printed silver electrode (SPSE). The iron oxide (Fe₃O₄) nanoparticles were characterized with UV-visible spectroscopy, Fourier-Transform infrared spectroscopy (FT-IR) and Scanning electron microscopy (SEM) prior to the modification of the SPSE. The EP oxidation peak current (Iap) increased with an increase in the concentration of EP as well as the scan rate (from 25 - 400 mVs⁻¹). Using cyclic voltammetry (CV), the relationship between Iap and EP concentration was linear over a range of 3.8 -118.9 µM and 118.9-175 µM with a detection limit of 41.99 µM and 83.16 µM, respectively. Selective detection of EP in the presence of ascorbic acid was also achieved at this electrode.

Keywords: screenprint electrode, iron oxide nanoparticle, epinephrine, serum, cyclic voltametry

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Authors: Ergun Sakalar, Kubra Bilgic

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Faster detection and characterization of pathogens are the basis of the evoid from foodborne pathogens. Salmonella spp., Shigella spp. and Listeria monocytogenes are common foodborne bacteria that are among the most life-threatining. It is important to rapid and accurate detection of these pathogens to prevent food poisoning and outbreaks or to manage food chains. The present work promise to develop a sensitive, species specific and reliable PCR based detection system for simultaneous detection of Salmonella spp., Shigella spp. and Listeria monocytogenes. For this purpose, three genes were picked out, ompC for Salmonella spp., ipaH for Shigella spp. and hlyA for L. monocytogenes. After short pre-enrichment of milk was passed through a vacuum filter and bacterial DNA was exracted using commercially available kit GIDAGEN®(Turkey, İstanbul). Detection of amplicons was verified by examination of the melting temperature (Tm) that are 72° C, 78° C, 82° C for Salmonella spp., Shigella spp. and L. monocytogenes, respectively. The method specificity was checked against a group of bacteria strains, and also carried out sensitivity test resulting in under 10² CFU mL⁻¹ of milk for each bacteria strain. Our results show that the flourescence based triplex qPCR method can be used routinely to detect Salmonella spp., Shigella spp. and L. monocytogenes during the milk processing procedures in order to reduce cost, time of analysis and the risk of foodborne disease outbreaks.

Keywords: evagreen, food-born bacteria, pathogen detection, real-time pcr

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Bendiabdellah Azzeddine, Cherif Bilal Djamal Eddine

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The three-phase power converter voltage structure is widely used in many power applications but its failure can lead to partial or total loss of control of the phase currents and can cause serious system malfunctions or even a complete system shutdown. To ensure continuity of service in all circumstances, effective and rapid techniques of detection and location of inverter fault is to be implemented. The present paper is dedicated to open-circuit fault detection in a three-phase two-level inverter fed induction motor. For detection purpose, the proposed contribution addresses the Park’s current vectors associated to a polar coordinates calculation tool to compute the exact value of the fault angle corresponding directly to the faulty IGBT switch. The merit of the proposed contribution is illustrated by experimental results.

Keywords: diagnosis, detection, Park’s vectors, polar coordinates, open-circuit fault, inverter, IGBT switch

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Authors: Rana Gill, Chandandeep Kaur

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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.

Keywords: segmentation, edge detection, text, extracting characters

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Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: Peter U. Eze, P. Udaya, Robin J. Evans

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Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, p. The constant correlation p, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from p. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost.

Keywords: Constant Correlation, Medical Image, Spread Spectrum, Tamper Detection, Watermarking

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Authors: Salma M. Wakil

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Advances in the field of genetics overwhelmed detecting large number of inherited disorders at the molecular level and directed to the development of innovative technologies. These innovations have led to gene sequencing, prenatal mutation detection, pre-implantation genetic diagnosis; population based carrier screening and genome wide analyses using microarrays. Microarrays are widely used in establishing clinical and diagnostic setup for genetic anomalies at a massive level, with the advent of cytoscan molecular karyotyping as a clinical utility card for detecting chromosomal aberrations with high coverage across the entire human genome. Unlike a regular karyotype that relies on the microscopic inspection of chromosomes, molecular karyotyping with cytoscan constructs virtual chromosomes based on the copy number analysis of DNA which improves its resolution by 100-fold. We have been investigating a large number of patients with Developmental Delay and Intellectual disability with this platform for establishing micro syndrome deletions and have detected number of novel CNV’s in the Arabian population with the clinical relevance.

Keywords: microarrays, molecular karyotyping, developmental delay, genetics

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Authors: Ji Su Kim, Bo Ram Choi, Ju Yeon Cho, Hyukjin Lee

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Epidermal growth factor receptor (EGFR) 19 deletion mutation gene is over-expressed in carcinoma patient. EGFR 19 deletion mutation is known as typical biomarker of non-small cell lung cancer (NSCLC), which one section in the coding exon 19 of EGFR is deleted. Therefore, there have been many attempts over the years to detect EGFR 19 deletion mutation for replacing conventional diagnostic method such as PCR and tissue biopsy. We developed a simple and facile detection platform based on Rolling Circle Amplification (RCA), which provides highly amplified products in isothermal amplification of the ligated DNA template. Limit of detection (~50 nM) and a faster detection time (~30 min) could be achieved by introducing RCA.

Keywords: EGFR19, cancer, diagnosis, rolling circle amplification (RCA), hydrogel

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Authors: Inese Tenberga

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Undoubtedly, the focus of the present capitalist system of finance has shifted from the concept of productivity of money to the ‘cult of money’, which is characterized by such notions as speculative activity, squander, self-profit, vested interest, etc. The author is certain that a civilized society cannot follow this economic path any longer and therefore suggests that one solution would be to integrate the Islamic financial model in the banking sector of the EU to overcome its economic vulnerability and structurally transform its economies or build resilience against shocks and crisis. The researcher analyses the Islamic financial model, which is providing the basis for the concept of non-productivity of money, and proposes to consider it as a new paradigm of economic thinking. The author argues that it seeks to establish a broad-based economic well-being with an optimum rate of economic growth, socio-economic justice, equitable distribution of income and wealth. Furthermore, the author analyses and proposes to use the experience of member states of the Islamic Development Bank for the formation of a new EU interest free banking. It is offered to create within the EU banking system a credit sector and investment sector respectively. As a part of the latter, it is recommended to separate investment banks specializing in speculative investments and non­speculative investment banks. Meanwhile, understanding of the idea of Islamic banking exclusively from the perspective of the manner of yielding profit that differs from credit banking, without considering the legal, social, ethical guidelines of Islam impedes to value objectively the advantages of this type of financial activities at the non-Islamic jurisdictions. However, the author comes to the conclusion the imperative of justice and virtue, which is inherent to all of us, exists regardless of religion. The author concludes that the global community should adopt the experience of the Muslim countries and focus on the Islamic banking model.

Keywords: credit sector, EU banking system, investment sector, Islamic banking

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Tian Xia, Yuan Yan Tang

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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.

Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian

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Authors: Gaurav Kumar Sinha

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The oil and gas industry, characterized by its complex and global supply chains, faces significant challenges in ensuring security, transparency, and efficiency. Blockchain technology, with its decentralized and immutable ledger, offers a transformative solution to these issues. This paper explores the application of blockchain technology in the oil and gas supply chain, highlighting its potential to enhance data security, improve transparency, and streamline operations. By leveraging smart contracts, blockchain can automate and secure transactions, reducing the risk of fraud and errors. Additionally, the integration of blockchain with IoT devices enables real-time tracking and monitoring of assets, ensuring data accuracy and integrity throughout the supply chain. Case studies and pilot projects within the industry demonstrate the practical benefits and challenges of implementing blockchain solutions. The findings suggest that blockchain technology can significantly improve trust and collaboration among supply chain participants, ultimately leading to more efficient and resilient operations. This study provides valuable insights for industry stakeholders considering the adoption of blockchain technology to address their supply chain management challenges.

Keywords: blockchain technology, oil and gas supply chain, data security, transparency, smart contracts, IoT integration, real-time tracking, asset monitoring, fraud reduction, supply chain efficiency, data integrity, case studies, industry implementation, trust, collaboration.

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Authors: Laid Fekih

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Background: It is difficult to detect lying, deception, and misrepresentation just by looking at verbal or non-verbal expression during the criminal investigation process, as there is a common belief that it is possible to tell whether a person is lying or telling the truth just by looking at the way they act or behave. The process of detecting lies and deception during the criminal investigation process needs more studies and research to overcome the difficulties facing the investigators. Method: The present study aimed to identify the effectiveness of cognitive methods and techniques in detecting deception during the criminal investigation. It adopted the quasi-experimental method and covered a sample of (20) defendants distributed randomly into two homogeneous groups, an experimental group of (10) defendants be subject to criminal investigation by applying cognitive techniques to detect deception and a second experimental group of (10) defendants be subject to the direct investigation method. The tool that used is a guided interview based on models of investigative questions according to the cognitive deception detection approach, which consists of three techniques of Vrij: imposing the cognitive burden, encouragement to provide more information, and ask unexpected questions, and the Direct Investigation Method. Results: Results revealed a significant difference between the two groups in term of lie detection accuracy in favour of defendants be subject to criminal investigation by applying cognitive techniques, the cognitive deception detection approach produced superior total accuracy rates both with human observers and through an analysis of objective criteria. The cognitive deception detection approach produced superior accuracy results in truth detection: 71%, deception detection: 70% compared to a direct investigation method truth detection: 52%; deception detection: 49%. Conclusion: The study recommended if practitioners use a cognitive deception detection technique, they will correctly classify more individuals than when they use a direct investigation method.

Keywords: the cognitive lie detection approach, deception, criminal investigation, mental health

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Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti

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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.

Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports

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Authors: Elizabeth Snyman-Van Deventer

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Numerous corporate scandals, which included investment scams, corporate malfeasance, unethical conduct and conflicts of interest, contributed to the collapse of WorldCom, Global Crossing, Xerox, Tyco, Enron, Sprint, AbbVie and Imclone and led to alarmed investors abandoning public securities markets and the tumbling of U.S stock markets. These companies suffered significant financial losses due to substantial and fraudulent misstatements and other illegal, corrupt or unethical practices. Executives were convicted of fraud and sentenced to prison. The corporate financial scandals, governance failures, and the ensuing public outcries led to mandatory legislation, e.g. the Sarbanes-Oxley Act in the USA. In European corporate scandals such as Parmalat, Royal Dutch Ahold, Vivendi, Adecco and Elan, the boards missed financial misrepresentations. In South Africa, Steinhoff is the most well-known example of corporate collapse, but now we can also add Tongaat Hulett. It seems as if fraud and corruption may be the major sources of these corporate collapses. In most instances, there is either the active involvement of the directors and managers in these fraudulent or corrupt practices, or there is a negligent or even intentional failure to act by directors to prevent these activities. However, besides directors and managers, auditors and lawyers failed in most of these companies to fulfil their professional duties. In most of these major collapses, the ethics of especially auditors and directors could be questioned. This paper will first provide a brief overview of corporate collapses. Secondly, the reasons for these collapses, with a focus on unethical conduct, will be discussed.

Keywords: professional duties, corporate collapses, ethical conduct, legal ethics, directors, auditors

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Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Jaiyeop Lee

Abstract:

Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals.

Keywords: rapid detection, SARS-CoV-2, sensitive detection, wastewater surveillance

Procedia PDF Downloads 63

Authors: Mahdi Rahaie

Abstract:

MicroRNAs are a novel class of small RNAs which regulate gene expression by translational repression or degradation of messenger RNAs. To produce sensitive, simple and cost-effective assays for microRNAs, detection is in urgent demand due to important role of these biomolecules in progression of human disease such as Alzheimer’s, Multiple sclerosis, and some other neurodegenerative diseases. Herein, we report several novel, sensitive and specific microRNA nanobiosensors which were designed based on colorimetric and fluorescence detection of nanoparticles and hybridization chain reaction amplification as an enzyme-free amplification. These new strategies eliminate the need for enzymatic reactions, chemical changes, separation processes and sophisticated equipment whereas less limit of detection with most specify are acceptable. The important features of these methods are high sensitivity and specificity to differentiate between perfectly matched, mismatched and non-complementary target microRNAs and also decent response in the real sample analysis with blood plasma. These nanobiosensors can clinically be used not only for the early detection of neuro diseases but also for every sickness related to miRNAs by direct detection of the plasma microRNAs in real clinical samples, without a need for sample preparation, RNA extraction and/or amplification.

Keywords: hybridization chain reaction, microRNA, nanobiosensor, neurodegenerative diseases

Procedia PDF Downloads 135