Search results for: constant false alarm rate (CFAR)

Authors: Liang Zhao, Guangwen Wang, Guichang Liu

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Constant potometer is an important component of pipeline anti-corrosion systems in the chemical industry. Based on Internet of Things (IoT) technology, Programmable Logic Controller (PLC) technology and database technology, this paper developed a set of a constant potometer remote monitoring management system. The remote monitoring and remote adjustment of the working status of the constant potometer are realized. The system has real-time data display, historical data query, alarm push management, user permission management, and supporting Web access and mobile client application (APP) access. The actual engineering project test results show the stability of the system, which can be widely used in cathodic protection systems.

Keywords: internet of things, pipe corrosion protection, potentiostat, remote monitoring

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Authors: Liaquat Ahmad, Muhammad Aslam, Muhammad Azam

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In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart’s x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate.

Keywords: average run length, control charts, process shift, regression estimators, repetitive group sampling

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: Larbi Hammadi, N. Boudjenane, N. Benhallou, R. Houjedje, R. Reffis, M. Belhadri

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Many of clays exhibited the thixotropic behavior in which, the apparent viscosity of material decreases with time of shearing at constant shear rate. The structural kinetic model (SKM) was used to characterize the thixotropic behavior of two different kinds of clays used in fabrication of ceramic. Clays selected for analysis represent the fluid and semisolid clays materials. The SKM postulates that the change in the rheological behavior is associated with shear-induced breakdown of the internal structure of the clays. This model for the structure decay with time at constant shear rate assumes nth order kinetics for the decay of the material structure with a rate constant.

Keywords: ceramic, clays, structural kinetic model, thixotropy, viscosity

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Authors: Lucas Paganin, Viliam Makis

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With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

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Authors: Huseyin Sinan Gunesli

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Process Safety Management is a disciplined framework for managing the integrity of systems and processes that handle hazardous substances. It relies on good design principles, well-implemented automation systems, and operating and maintenance practices. Alarm Management Systems play a critically important role in the safe and efficient operation of modern industrial plants. In that respect, Alarm Management is one of the critical factors feeding the safe operations of the plants in the manner of applying effective process safety principles. Trans Anatolian Natural Gas Pipeline (TANAP) is part of the Southern Gas Corridor, which extends from the Caspian Sea to Italy. TANAP transports Natural Gas from the Shah Deniz gas field of Azerbaijan, and possibly from other neighboring countries, to Turkey and through Trans Adriatic Pipeline (TAP) Pipeline to Europe. TANAP plays a crucial role in maintaining Energy Security for the region and Europe. In that respect, the application of Process Safety principles is vital to deliver safe, reliable and efficient Natural Gas delivery to Shippers both in the region and Europe. Effective Alarm Management is one of those Process Safety principles which feeds safe operations of the TANAP pipeline. Alarm Philosophy was designed and implemented in TANAP Pipeline according to the relevant standards. However, it is essential to manage the alarms received in the control room effectively to maintain safe operations. In that respect, TANAP has commenced Alarm Management & Rationalization program as of February 2022 after transferring to Plateau Regime, reaching the design parameters. While Alarm Rationalization started, there were more than circa 2300 alarms received per hour from one of the compressor stations. After applying alarm management principles such as reviewing and removal of bad actors, standing, stale, chattering, fleeting alarms, comprehensive review and revision of alarm set points through a change management principle, conducting alarm audits/design verification and etc., it has been achieved to reduce down to circa 40 alarms per hour. After the successful implementation of alarm management principles as specified above, the number of alarms has been reduced to industry standards. That significantly improved operator vigilance to focus on mainly important and critical alarms to avoid any excursion beyond safe operating limits leading to any potential process safety events. Following the ‟What Gets Measured, Gets Managed” principle, TANAP has identified key Performance Indicators (KPIs) to manage Process Safety principles effectively, where Alarm Management has formed one of the key parameters of those KPIs. However, review and analysis of the alarms were performed manually. Without utilizing Alarm Management Software, achieving full compliance with international standards is almost infeasible. In that respect, TANAP has started using one of the industry-wide known Alarm Management Applications to maintain full review and analysis of alarms and define actions as required. That actually significantly empowered TANAP’s process safety principles in terms of Alarm Management.

Keywords: process safety principles, energy security, natural gas pipeline operations, alarm rationalization, alarm management, alarm management application

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Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

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A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Lae-Jeong Park

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The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

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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: Mahdi Pirmoradian, Olayinka Adigun, Christos Politis

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Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors.

Keywords: cooperative networks, normalized capacity, sensing time

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Authors: Obokhai Kess Asikhia

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This work explores design concept generation by understanding user needs through observation and interview. The aim is to examine several principles and guidelines in obtaining evidence from observing how users interact with the targeted product and interviewing them to acquire deep insights of their needs. With the help of Quality Function Deployment (QFD), the identified needs of the users while interacting with the product were ranked using the normalised weighting approach. Furthermore, a low fidelity prototype of the alarm clock is developed with a view of addressing the identified needs of the users. Finally, the low fidelity prototype design was evaluated with two design prototypes already existing in the market through a study involving 30 participants. Preliminary results reveal higher performance ratings by the majority of the participants of the new prototype compared to the other existing alarm clocks in the market used in the study.

Keywords: design concept, low fidelity prototype, normalised weighting approach, quality function deployment, user needs

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Authors: Syed Dawood Md. Taimur, Saidur Rahman Khan, Farzana Islam

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Objective: To evaluate the factors which predetermine the coronary artery disease in patients having positive Exercise Tolerance Test (ETT) that is treadmill results and coronary artery findings. Methods: This descriptive study was conducted at Department of Cardiology, Ibrahim Cardiac Hospital & Research Institute,Dhaka,Bangladesh from 1st January, 2014 to 31st August, 2014. All patients who had done ETT (treadmill) for chest pain diagnosis were studied. One hundred and four patients underwent coronary angiogram after positive treadmill result. Patients were divided into two groups depending upon the angiographic findings, i.e. true positive and false positive. Positive treadmill test patients who have coronary artery involvement these are called true positive and who have no involvement they are called false positive group. Both groups were compared with each other. Results: Out of 104 patients, 81 (77.9%) patients had true positive ETT and 23 (22.1%) patients had false positive ETT. The mean age of patients in positive ETT was 53.46± 8.06 years and male mean age was 53.63±8.36 years and female was 52.87 ± 7.0 years. Sixty nine (85.19%) male patients and twelve (14.81%) female patients had true positive ETT, whereas 15 (65.21%) males and 8 (34.79%) females had false positive ETT, this was statistically significant (p<0.032)difference in the two groups(sex) in comparison of true and false positive ETT. The risk factors of these patients like diabetes mellitus, hypertension, dyslipidemia, family history and smoking were seen among these patients. Hypertensive patients having true positive which were statically significant (p<0.004) and diabetic, dyslipidaemic patients having true positive which were statically significant (p < 0.032 & 0.030).True positive patients had family history were 68 (83.95%) and smoking were 52 (64.20%), where family history patients had statistically significant(p<0.017) between two groups of patients and smokers were significant (p<0.012). 46 true positive patients achieved THR which was not statistically significant (P<0.138)and 79 true patients had abnormal resting ECG whether it was significant (p<0.036). Amongst the vessels involvement the most common was LAD 55 (67.90%), followed by LCX 42 (51.85%), RCA 36 (44.44%) and the LMCA was 9 (11.11%), .40 patients (49.38%) had SVD, 26 (30.10%) had DVD, 15(18.52%) had TVD and 23 had normal coronary arteries. Conclusion: It can be concluded that among the female patients who have positive ETT with normal resting ECG, who had achieved target heart rate are likely to have a false positive test result. Conversely male patients,resting abnormal ECG who had not achieved THR, symptom limited ETT, have a hypertension, diabetis, dyslipidaemic, family history and smoking are likely to have a true positive treadmill test result.

Keywords: exercise tolerance test, coronary artery disease, coronary angiography, true positive, false positive

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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Authors: Sibashish Baksi, Ujjaini Sarkar, Sudeshna Saha

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In the present study, hydrolysis of Pinus roxburghi wood powder was carried out with Viscozyme, and kinetics of the hydrolysis has been investigated. Finely ground sawdust is submerged into 2% aqueous peroxide solution (pH=11.5) and pretreated through autoclaving, probe sonication, and alkaline peroxide pretreatment. Afterward, the pretreated material is subjected to hydrolysis. A chain of experiments was executed with delignified biomass (50 g/l) and varying enzyme concentrations (24.2–60.5 g/l). In the present study, 14.32 g/l of glucose, along with 7.35 g/l of xylose, have been recovered with a viscozyme concentration of 48.8 g/l and the same condition was treated as optimum condition. Additionally, thermal deactivation of viscozyme has been investigated and found to be gradually decreasing with escalated enzyme loading from 48.4 g/l (dissociation constant= 0.05 h⁻¹) to 60.5 g/l (dissociation constant= 0.02 h⁻¹). The hydrolysis reaction is a pseudo first-order reaction, and therefore, the rate of the hydrolysis can be expressed as a fractal-like kinetic equation that communicates between the product concentration and hydrolytic time t. It is seen that the value of rate constant (K) increases from 0.008 to 0.017 with augmented enzyme concentration from 24.2 g/l to 60.5 g/l. Greater value of K is associated with stronger enzyme binding capacity of the substrate mass. However, escalated concentration of supplied enzyme ensures improved interaction with more substrate molecules resulting in an enhanced de-polymerization of the polymeric sugar chains per unit time which eventually modifies the physiochemical structure of biomass. All fractal dimensions are in between 0 and 1. Lower the value of fractal dimension, more easily the biomass get hydrolyzed. It can be seen that with increased enzyme concentration from 24.2 g/l to 48.4 g/l, the values of fractal dimension go down from 0.1 to 0.044. This indicates that the presence of more enzyme molecules can more easily hydrolyze the substrate. However, an increased value has been observed with a further increment of enzyme concentration to 60.5g/l because of diffusional limitation. It is evident that the hydrolysis reaction system is a heterogeneous organization, and the product formation rate depends strongly on the enzyme diffusion resistances caused by the rate-limiting structures of the substrate-enzyme complex. Value of the rate constant increases from 1.061 to 2.610 with escalated enzyme concentration from 24.2 to 48.4 g/l. As the rate constant is proportional to Fick’s diffusion coefficient, it can be assumed that with a higher concentration of enzyme, a larger amount of enzyme mass dM diffuses into the substrate through the surface dF per unit time dt. Therefore, a higher rate constant value is associated with a faster diffusion of enzyme into the substrate. Regression analysis of time curves with various enzyme concentrations shows that diffusion resistant constant increases from 0.3 to 0.51 for the first two enzyme concentrations and again decreases with enzyme concentration of 60.5 g/l. During diffusion in a differential scale, the enzyme also experiences a greater resistance during diffusion of larger dM through dF in dt.

Keywords: viscozyme, glucose, fractal kinetics, thermal deactivation

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Authors: Eduardo C. Guardia, Jose W. M. Lima, Afonso H. M. Santos

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This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.

Keywords: bathtub curve, failure analysis, lifetime estimation, parameter estimation, Weibull distribution

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Authors: Mohsen Azarmjoo, Mehdi Mehdizadeh Koupaei, Maryam Mehdizadeh Koupaei, Asghar Mahdlouei Azar

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The increasing prevalence of wireless networks in our daily lives has made them indispensable. The aim of this research is to investigate the management of information transfer in wireless networks and the integration of renewable solar energy resources in a residential building. The focus is on the transmission of electricity and information through wireless networks, as well as the utilization of sensors and wireless fire alarm systems. The research employs a descriptive approach to examine the transmission of electricity and information on a wireless network with electric and optical telephone lines. It also investigates the transmission of signals from sensors and wireless fire alarm systems via radio waves. The methodology includes a detailed analysis of security, comfort conditions, and costs related to the utilization of wireless networks and renewable solar energy resources. The study reveals that it is feasible to transmit electricity on a network cable using two pairs of network cables without the need for separate power cabling. Additionally, the integration of renewable solar energy systems in residential buildings can reduce dependence on traditional energy carriers. The use of sensors and wireless remote information processing can enhance the safety and efficiency of energy usage in buildings and the surrounding spaces.

Keywords: renewable energy, intelligentization, wireless sensors, fire alarm system

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Authors: Mehmet Izzettin Yilmazer, Emin Cadirli

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Zn-7wt.%Al-2.96wt.%Cu eutectic alloy was directionally solidified upwards with different temperature gradients (from 6.70 K/mm to 10.67 K/mm) at a constant growth rate (16.4 Km/s) and also different growth rate (from 8.3 micron/s to 166 micron/s) at a constant temperature gradient (10.67 K/mm) using a Bridgman–type growth apparatus.The values of eutectic spacing were measured from longitudinal and transverse sections of the samples. The dependency of microstructures on the G and V were determined with linear regression analysis and experimental equations were found as λl=8.953xVexp-0.49, λt=5.942xVexp-0.42 and λl=0.008xGexp-1.23, λt=0.024xGexp-0.93. The measurements of microhardness of directionally solidified samples were obtained by using a microhardness test device. The dependence of microhardness HV on temperature gradient and growth rate were analyzed. The dependency of microhardness on the G and V were also determined with linear regression analysis as HVl=110.66xVexp0.02, HVt=111.94xVexp0.02 and HVl=69.66xGexp0.17, HVt=68.86xGexp0.18. The experimental results show that the microhardness of the directionally solidified Zn-Al-Cu alloy increases with increasing the growth rate. The results obtained in this work were compared with the previous similar experimental results.

Keywords: directional solidification, eutectic alloys, microstructure, microhardness

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Authors: Seyed Abolhassan Naeini, Ali Namaei

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This investigation presents the behavior of the unsaturated silty sand by calculating the shear resistance of the specimens by numerical method. In order to investigate this behavior, a series of triaxial tests have been simulated in constant water condition. The finite difference software FLAC3D has been carried out for analyzing the shear resistance and the results are compared with findings from a previous laboratory tests. Constant water tests correspond to a field condition where the rate of the loading is much quicker than the rate at which the pore water is able to drain out of the soil. Tests were simulated on two groups of the silty sands. The obtained results show that the FLAC software may be able to simulate the behavior of specimens with the low suction value magnitude. As the initial suction increased, the differences between numerical and experimental results increased, especially in loose sand. Since some assumptions were used for input parameters, a conclusive result needs more investigations.

Keywords: finite difference, shear resistance, unsaturated silty sand, constant water test

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Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

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Authors: M. Ranjeeth, S. Anuradha

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Spectrum sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. In this paper we compare the theoretical results of detection probability of different fading environments like Rayleigh, Rician, Nakagami-m fading channels with the simulation results using energy detection based spectrum sensing. The numerical results are plotted as P_f Vs P_d for different SNR values, fading parameters. It is observed that Nakagami fading channel performance is better than other fading channels by using energy detection in spectrum sensing. A MATLAB simulation test bench has been implemented to know the performance of energy detection in different fading channel environment.

Keywords: spectrum sensing, energy detection, fading channels, probability of detection, probability of false alarm

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Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

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Authors: Kate Whitman, Zahra Murad, Joe Cox, Adam Cox

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This study investigates the influence of normative communications on digital piracy intentions. Although descriptive norms are thought to influence behavior, the study examines the potential bias in one's own behavior, leading to false consensus—a phenomenon perpetuating undesirable activities. The research tests the presence of false consensus and the effect of correcting normative predictions on changes in piracy intentions, examining gender differences. Results from a controlled experiment (N = 684) indicate that normative communications, reflecting the "real" norm based on government data (N=5000), increase (decrease) piracy intentions among men (women) underestimating their peers' behavior. Conversely, neither men nor women overestimating their peers' piracy show any notable change in intentions. Considering men consume more illegal content than women, suggesting they pose a higher risk, the study highlights the need for cautious use of normative communications. Therefore, policymakers should minimize the visibility of piracy behavior for effective digital piracy management.

Keywords: digital piracy, false consensus, normative interventions, persuasive messages

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Authors: Suparpit von Bormann, Winai Sayorwan, Sirichai Channim, Sararat Rungruangkhanarak, Premchai Suksamran, Piyaporn Srisuk, Piyatida Phosri

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The best medicine will not work if the patient does not take them. There are several methods developed to help patients to be adherent to medicine. However, non-adherent rate still high: 24% in physically ill and 42% in mentally ill patients. Moreover, patients might feel less confident when carrying medicine around. Normal medicine box has no alarm; whereas the one with alarm is not handy and might be left at home. Therefore, Medication Reminder (MR) was invented. MR is a medicine pocket that has an alarm clock to remind the patient when it is the time to take medicine. It also has a small light indicating the medicine the patient has to take. This pocket is attached within a purse or wallet because most people forget medicine but do not forget to take their money. This research was conducted to develop innovation assisting patients to take their medicine on time. Samples were 24 volunteers who went out to work every day. Uncoated tablets, coated tablets, and capsules were filled in three types of containers: MR, plastic bag with ziplock, and normal plastic box. Each volunteer carried three types of containers everywhere during day time. After three days, medicines were tested for physical quality (appearance, odor, color, hardness, and weight) in laboratory. Medication adherence and satisfaction questionnaires were completed by participants. The results showed that MR showed significant improvement in participants’ adherence than plastic bag with ziplock, and normal plastic box at p < .001 (x̄(SD) = 11.16(0.75), 7.83(0.98), 8.83(1.32), respectively). Based on the quality test, MR and normal plastic box significantly better protected medicine than plastic bag with zip lock at p < .001 (x̄(SD) = 4(0.00), 4(0.00), 2.5(0.54), respectively). Most participants were satisfied with the innovation in highest level (4.50 out of 5). MR has a potential to improve adherent rates of participants and therefore to be an innovation that helps reducing the cost of treatment due to non-adherence. MR also has a potential in commercial aspect due to its effects in preserving quality of medicine. MR can be integrated with local products such as silk purse that can increase income for local people.

Keywords: medication, reminder, adherence, satisfaction

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Authors: Bandar Ali Al-Asbahi, Mohammad Hafizuddin Haji Jumali

Abstract:

Forster energy transfer between poly (9,9'-di-n-octylfluorenyl-2,7-diyl) (PFO)/TiO2 nanoparticles (NPs) as a donor and Fluorol 7GA as an acceptor has been studied. The energy transfer parameters were calculated by using mathematical models. The dominant mechanism responsible for the energy transfer between the donor and acceptor molecules was Forster-type, as evidenced by large values of quenching rate constant, energy transfer rate constant and critical distance of energy transfer. Moreover, these composites which were used as an emissive layer in organic light emitting diodes, were investigated in terms of current density–voltage and electroluminescence spectra.

Keywords: energy transfer parameters, forster-type, electroluminescence, organic light emitting diodes

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Authors: Mohd. Akram, A. A. M. Saeed

Abstract:

Surfactants are widely used in our daily life either directly in household and personal care products or indirectly in the industrial processes. The kinetics of the interaction of glycyl-DL-aspartic acid (Gly-DL-Asp) with ninhydrin has been investigated spectrophotometrically in aqueous and organic-solvent media in the absence and presence of cationic surfactant of tetradecyltrimethylammonium bromide (TTAB). The study was carried out under different experimental conditions. The first and fractional order-rate were observed for [Gly-DL-Asp] and [ninhydrin], respectively. The reaction was enhanced about four-fold by TTAB micelles. The effect of organic solvents was studied at a constant concentration of TTAB and showed an increase in the absorbance as well as the rate constant for the formation of product (Ruhemann's purple). The results obtained in micellar media are treated quantitatively in terms of pseudo-phase and Piszkiewicz cooperativity models. The Arrhenius and Eyring equations are valid for the reaction over the range of temperatures used and different activation parameters (Ea, ∆H#, ∆S#, and ∆G#) have been evaluated.

Keywords: glycyl-DL-aspartic acid, ninhydrin, organic solvents, TTAB

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Authors: Retno Ambarwati Sigit Lestari, Wahyudi Budi Sediawan, Sarto Sarto

Abstract:

This study focused on the removal of sulfide from liquid solution using biofilm on packed bed of salak fruit seeds. Biofilter operation of 444 hours consists of 6 phases of operation. Each phase lasted for approximately 72 hours to 82 hours and run at various inlet concentration and flow rate. The highest removal efficiency is 92.01%, at the end of phase 7 at the inlet concentration of 60 ppm and the flow rate of 30 mL min-1. Mathematic model of sulfide removal was proposed to describe the operation of biofilter. The model proposed can be applied to describe the removal of sulfide liquid using biofilter in packed bed. The simulation results the value of the parameters in process. The value of the rate maximum spesific growth is 4.15E-8 s-1, Saturation constant is 9.1E-8 g cm-3, mass transfer coefisient of liquid is 0.5 cm s-1, Henry’s constant is 0.007, and mass of microorganisms growth to mass of sulfide comsumed is 30. The value of the rate maximum spesific growth in early process is 0.00000004 s-1.

Keywords: biofilm, packed bed, removal, sulfide, salak fruit seeds.

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Authors: Lamis Naddaf, Yuval Tabach

Abstract:

In this research, we identified the missense genetic variants that have the potential to enhance resistance against cancer. Such field has not been widely explored, as researchers tend to investigate mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution, and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and can have significant implications on improved risk estimation, diagnostics, prognosis and even for personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and picked up the alleles that showed a correlation with the species’ cancer resistance. We predicted 250 protecting variants (PVs) with a 0.01 false discovery rate and more than 20 thousand PVs with a 0.25 false discovery rate. Cancer resistance in Mammals and reptiles was significantly predicted by the number of PVs a species has. Moreover, Genes enriched with the protecting variants are enriched in pathways relevant to tumor suppression like pathways of Hedgehog signaling and silencing, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are more abundant in healthy people compared to cancer patients within different human races.

Keywords: comparative genomics, machine learning, cancer resistance, cancer-protecting alleles

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

Abstract:

The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

Procedia PDF Downloads 75