Search results for: deep convolutional neural networks

Authors: Ibhade Oluwabunlola Adisa Ibojo, Tolulope Funmilola Aladetan

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Children living with albinism in Nigeria face significant legal and social challenges that threaten their rights and well-being. Despite existing laws aimed at protecting the rights of children, including the Child Rights Act of 2003, the unique vulnerabilities of children with albinism often go unaddressed. This abstract explores the legal status of these children, highlighting the gaps in legal protection and the prevalence of discrimination and violence against them. In many Nigerian communities, deep-seated myths and superstitions regarding albinism contribute to the marginalization and stigmatization of individuals with this condition. Consequently, children with albinism are at a heightened risk of violence, including abduction and ritualistic killings, often with impunity for the perpetrators. This situation is exacerbated by inadequate legal frameworks, ineffective enforcement of existing laws, and a lack of awareness among law enforcement officials and the general public. The paper also examines the implications of these challenges on the rights of children with albinism to life, education, and healthcare. Recommendations are proposed for improving the legal framework and implementing targeted awareness campaigns to protect these vulnerable children. By addressing these issues, the Nigerian legal system can better safeguard the rights and dignity of children living with albinism, ensuring they can lead safe and fulfilling lives. This research aims to raise awareness of the plight of these children and advocate for stronger legal protections to promote their rights and well-being in Nigerian society.

Keywords: Albinism, vulnerable, children, laws

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: M. N. Islam, B. Boswell, Y. R. Ginting

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The influence of canned cycles and cutting parameters on hole quality in cryogenic drilling has been investigated experimentally and analytically. A three-level, three-parameter experiment was conducted by using the design-of-experiment methodology. The three levels of independent input parameters were the following: for canned cycles—a chip-breaking canned cycle (G73), a spot drilling canned cycle (G81), and a deep hole canned cycle (G83); for feed rates—0.2, 0.3, and 0.4 mm/rev; and for cutting speeds—60, 75, and 100 m/min. The selected work and tool materials were aluminum 6061-6T and high-speed steel (HSS), respectively. For cryogenic cooling, liquid nitrogen (LN2) was used and was applied externally. The measured output parameters were the three widely used quality characteristics of drilled holes—diameter error, circularity, and surface roughness. Pareto ANOVA was applied for analyzing the results. The findings revealed that the canned cycle has a significant effect on diameter error (contribution ratio 44.09%) and small effects on circularity and surface finish (contribution ratio 7.25% and 6.60%, respectively). The best results for the dimensional accuracy and surface roughness were achieved by G81. G73 produced the best circularity results; however, for dimensional accuracy, it was the worst level.

Keywords: circularity, diameter error, drilling canned cycle, pareto ANOVA, surface roughness

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Authors: Ahmed Elbeheri, Tarek Zayed

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Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it’s the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage.

Keywords: steel bridge, bridge inspection, steel corrosion, image processing

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Authors: V. K. Arora, Suraj Prakash

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Pile foundations are provided to transfer the vertical and horizontal loads of superstructures like high rise buildings, bridges, offshore structures etc. to the deep strata in the soil. These vertical and horizontal loads are due to the loads coming from the superstructure and wind, water thrust, earthquake, and earth pressure, respectively. In a pile foundation, piles are used in groups. Vertical piles in a group of piles are more efficient to take vertical loads as compared to horizontal loads and when the horizontal load per pile exceeds the bearing capacity of the vertical piles in that case batter piles are used with vertical piles because batter piles can take more lateral loads than vertical piles. In this paper, a model study was conducted on three vertical pile group with single positive and negative battered pile subjected to lateral loads. The batter angle for battered piles was ±35◦ with the vertical axis. Piles were spaced at 2.5d (d=diameter of pile) to each other. The soil used for model test was cohesionless soil. Lateral loads were applied in three stages on all the pile groups individually and it was found that under the repeated action of lateral loading, the deflection of the piles increased under the same loading. After comparing the results, it was found that the pile group with positive batter pile fails at 28 kgf and the pile group with negative batter pile fails at 24 kgf so it shows that positive battered piles are stronger than the negative battered piles.

Keywords: vertical piles, positive battered piles, negative battered piles, cohesionless soil, lateral loads, model test

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Authors: Jatesh Kathpalia, Subhash Chander

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Violence against women has spread into a global epidemic. This has debilitating effect over the performance of women. Due to deep-rooted values, traditional Indian culture women fear the consequences of reporting violence and declare an unwillingness to subject themselves to the shame of being identified as battered women. Main interest was to study types of domestic violence which women face and to encourage them to report the matter. The study involved understanding the nature, extent and types of domestic violence. Two hundred rural women respondents were selected at random, interview schedule was prepared, and victims afflicted with domestic violence were identified. Data were collected and analyzed for different forms of domestic violence faced by women. 60% of the respondents faced domestic violence in different forms. Out of 120 women who were affected, 92.5% faced emotional, 90.8% faced verbal, 49.1% faced economic and 58.3% faced physical violence. 45.0% faced violence within three months of the marriage. Out of these, only 6.6% reported the violence to the police. Frequently faced forms of violence were slapping (27.1%), beating (24.3%) and starvation (25.7%). Number of women who were not allowed to spend money of their own stood at 30.5%. About 50% victims of emotional violence were facing constant criticism by their in-laws. Significant association was found between age, education and socio-economic status of the respondents and domestic violence. Rural women in Haryana face grave problem of domestic violence which need to be curbed for improving condition of women in society.

Keywords: domestic violence against women, economic, emotional, physical and verbal violence, marriage, rural women

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Syue-Liang Lin, C. Allen Chang

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Luminescence imaging is an important technique used in biomedical research and clinical diagnostic applications in recent years. Concurrently, the development of NIR luminescence probes / imaging contrast agents has helped the understanding of the structural and functional properties of cells and animals. Photodynamic therapy (PDT) is used clinically to treat a wide range of medical conditions, but the therapeutic efficacy of general PDT for deeper tumor was limited by the penetration of excitation source. The tumor targeting biomedical nanomaterials UCNP@PS (upconversion nanoparticle conjugated with photosensitizer) for photodynamic therapy and near-infrared imaging of cancer will be developed in our study. Synthesis and characterization of biomedical nanomaterials were completed in this studies. The spectrum of UCNP was characterized by photoluminescence spectroscopy and the morphology was characterized by Transmission Electron Microscope (TEM). TEM and XRD analyses indicated that these nanoparticles are about 20~50 nm with hexagonal phase. NaYF₄:Ln³⁺ (Ln= Yb, Nd, Er) upconversion nanoparticles (UCNPs) with core / shell structure, synthesized by thermal decomposition method in 300°C, have the ability to emit visible light (upconversion: 540 nm, 660 nm) and near-infrared with longer wavelength (downconversion: NIR: 980 nm, 1525 nm) by absorbing 800 nm NIR laser. The information obtained from these studies would be very useful for applications of these nanomaterials for bio-luminescence imaging and photodynamic therapy of deep tumor tissue in the future.

Keywords: Near Infrared (NIR), lanthanide, core-shell structure, upconversion, theranostics

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Authors: Morsy Ahmed Morsy Ismail

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In this paper, the structure of a coherent homodyne receiver for the Binary Phase Shift Keying (BPSK) Optical Code Division Multiple Access (OCDMA) network is introduced based on the Multi-Length Weighted Modified Prime Code (ML-WMPC) for multimedia applications. The Bit Error Rate (BER) of this homodyne detection is evaluated as a function of the number of active users and the signal to noise ratio for different code lengths according to the multimedia application such as audio, voice, and video. Besides, the Mach-Zehnder interferometer is used as an external phase modulator in homodyne detection. Furthermore, the Multiple Access Interference (MAI) and the receiver noise in a shot-noise limited regime are taken into consideration in the BER calculations.

Keywords: OCDMA networks, bit error rate, multiple access interference, binary phase-shift keying, multimedia

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Authors: Abhinav Sirvaiya, Karan Gupta, Pankaj Garg

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The demand of energy is increasing at every part of the world. Thus, use of fossil fuel is efficient which results in large liberation of carbon dioxide in atmosphere. Tons of this CO2 raises the risk of dangerous climate changes. To minimize the risk carbon capture and storage (CCS) has to be used so that the emitted carbon dioxide do not reach the atmosphere. CCS is being considered as one of the options that could have a major role to play in India.With the growing awareness towards the global warming, carbon capture and sequestration has a great importance. New technologies and theories are in use to capture CO2. This paper contains the methodology and technologies that is in use to capture carbon dioxide in India. The present scenario of CCS is also being discussed. CCS is playing a major role in enhancing recovery of oil (ERO). Both the purpose 1) minimizing percentage of carbon dioxide in atmosphere and 2) enhancing recovery of oil are fulfilled from the CCS. The CO2 is usually captured from coal based power plant and from some industrial sources and then stored in the geological formations like oil and gas reservoir and deep aquifers or in oceans. India has large reservoirs of coal which are being used for storing CO2, as coal is a good absorbent of CO2. New technologies and studies are going on for injection purposes. Government has initiated new plans for CCS as CCS is technically feasible and economically attractive. A discussion is done on new schemes that should bring up CCS plans and approaches. Stakeholders are welcomed for suitability of CCS. There is still a need to potentially capture the CO2 and avail its storage in developing country like India.

Keywords: Carbon Capture and Storage (CCS), carbon dioxide (CO2), enhance oil recovery, geological formations, stakeholders

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Ahmad E. Aldousaria, Abdulla Al Kafy

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Urbanization is an incessant process that involves the transformation of land use/land cover (LULC), resulting in a reduction of cool land covers and thermal comfort zones (TCZs). This study explores the directional shrinkage of TCZs in Kuwait using Landsat satellite data from 1991 – 2021 to predict the future LULC and TCZ distribution for 2026 and 2031 using cellular automata (CA) and artificial neural network (ANN) algorithms. Analysis revealed a rapid urban expansion (40 %) in SE, NE, and NW directions and TCZ shrinkage in N – NW and SW directions with 25 % of the very uncomfortable area. The predicted result showed an urban area increase from 44 % in 2021 to 47 % and 52 % in 2026 and 2031, respectively, where uncomfortable zones were found to be concentrated around urban areas and bare lands in N – NE and N – NW directions. This study proposes an effective and sustainable framework to control TCZ shrinkage, including zero soil policies, planned landscape design, manmade water bodies, and rooftop gardens. This study will help urban planners and policymakers to make Kuwait an eco–friendly, functional, and sustainable country.

Keywords: land cover change, thermal environment, green cover loss, machine learning, remote sensing

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: Lee Bih Ni, Elvis Fung

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This article is to review and understand the new generation of students to understand their expectations and attitudes. There are a group of students on school projects, creative work, educational software and digital signal source, the use of social networking tools to communicate with friends and a part in the competition. Today's students have been described as the new millennium students. They use information and communication technology in a more creative and innovative at home than at school, because the information and communication technologies for different purposes, in the home, usually occur in school. They collaborate and communicate more effectively when they are at home. Most children enter school, they will bring about how to use information and communication technologies, some basic skills and some tips on how to use information and communication technology will provide a more advanced than most of the school's expectations. Many teachers can help students, however, still a lot of work, "tradition", without a computer, and did not see the "new social computing networks describe young people to learn and new ways of working life in the future", in the education system of the benefits of using a computer.

Keywords: ICT education, digital history, new generation of students, benefits of using a computer

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: beam structures, layerwise, optimization, variable stiffness

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Authors: S. Farid F. Mojtahedi, Ali Khosravi, Behnaz Naeimian, S. Adel A. Hosseini

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Land subsidence is a gradual settling or sudden sinking of the land surface from changes that take place underground. There are different causes of land subsidence; most notably, ground-water overdraft and severe weather conditions. Subsidence of the land surface due to ground water overdraft is caused by an increase in the intergranular pressure in unconsolidated aquifers, which results in a loss of buoyancy of solid particles in the zone dewatered by the falling water table and accordingly compaction of the aquifer. On the other hand, exploitation of underground water may result in significant changes in degree of saturation of soil layers above the water table, increasing the effective stress in these layers, and considerable soil settlements. This study focuses on estimation of soil moisture at surface using different methods. Specifically, different methods for the estimation of moisture content at the soil surface, as an important term to solve Richard’s equation and estimate soil moisture profile are presented, and their results are discussed through comparison with field measurements obtained from Yanco1 station in south-eastern Australia. Surface soil moisture is not easy to measure at the spatial scale of a catchment. Due to the heterogeneity of soil type, land use, and topography, surface soil moisture may change considerably in space and time.

Keywords: artificial neural network, empirical method, remote sensing, surface soil moisture, unsaturated soil

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Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Luisa Müting, Wisnu Harto Adiwijoyo

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Agroforestry systems transform the agricultural landscapes in the Sahel region of Africa, providing food and farming products consumed for subsistence or sold for income. In the incrementally dry climate of the Sahel region, the spreading of agroforestry practices is integral for policymaker efforts to counteract land degradation and provide soil restoration in the region. Several measures on agroforestry practices have been implemented in the region by governmental and non-governmental institutions in recent years. However, despite the efforts, past research shows that awareness of how policies and interventions are being consumed and perceived by the public remains low. Therefore, interpreting public policy dilemmas by analyzing the public perception regarding agroforestry concepts and practices is necessary. Public perceptions and discourses can be an essential driver or constraint for the adoption of agroforestry practices in the region. Thus, understanding the public discourse behavior of crucial stakeholders could assist policymakers in developing inclusive and contextual policies that are relevant to the context of agroforestry adoption in Sahel region. To answer how information about agroforestry spreads and is perceived by the public. As internet usage increased drastically over the past decade, reaching a share of 33 percent of the population being connected to the internet, this research is based on online conversation data. Social media data from Facebook are gathered daily between April 2021 and April 2022 in Djibouti, Senegal, Mali, and Nigeria based on their share of active internet users compared to other countries in the Sahel region. A systematic methodology was applied to the extracted social media using discourse network analysis (DNA). This study then clustered the data by the types of agroforestry practices, sentiments, and country. Additionally, this research extracted the text data from online news media during the same period to pinpoint events related to the topic of agroforestry. The preliminary result indicates that tree management, crops, and livestock integration, diversifying species and genetic resources, and focusing on interactions and productivity across the agricultural system; are the most notable keywords in agroforestry-related conversations within the four countries in the Sahel region. Additionally, approximately 84 percent of the discussions were still dominated by big actors, such as NGO or government actors. Furthermore, as a subject of communication within agroforestry discourse, the Great Green Wall initiative generates almost 60 percent positive sentiment within the captured social media data, effectively having a more significant outreach than general agroforestry topics. This study provides an understanding for scholars and policymakers with a springboard for further research or policy design on agroforestry in the four countries of the Sahel region with systematically uncaptured novel data from the internet.

Keywords: sahel, djibouti, senegal, mali, nigeria, social networks analysis, public discourse analysis, sentiment analysis, content analysis, social media, online news, agroforestry, land restoration

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Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

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This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

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Authors: Christiana Ebobo

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Cybercrime consists of numerous crimes that are perpetrated on the internet on daily basis. The forms include but not limited to Identity theft, Pretentious dating, Desktop counterfeiting, Internet chat room, Cyber harassment, Fraudulent electronic mails, Automated Teller Machine Spoofing, Pornography, Piracy, Hacking, Credit card frauds, Phishing and Spamming. The general term used among the youths for this type of crime in Nigeria is ‘Yahoo Yahoo’. Cybercrime is on the increase among the youths at all levels as such this study aims at examining the impact of cybercrime on youth development in Nigeria. The study examines the impact of cybercrime on youths’ academic performance, integrity, employment and religious practices. The study is a survey which made use of questionnaire and focus group discussion among 150 randomly selected youths in Gwagwalada LCDA, Federal Capital Territory, Nigeria. The study adopts the systems theory as its theoretical framework. The study also adopts the simple frequency table and percentage for its data analysis. The study reveals that cybercrime has eaten deep into the minds of some youths and some of them are practicing diabolic means to succeed in it. It is also reveals that majority (68%) of the respondents believe that cybercrime impacts negatively on youths’ academic performance in Nigeria. The major recommendation of this study is that cybercrime offenders should be treated like armed robbers in order to discourage other youths from getting involved in it.

Keywords: armed robber, cybercrime, integrity, youth

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Authors: Adeniran K. Ademuwagun, Alastair Allen

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The relationship dependence between RSS and distance in an enclosed environment is an important consideration because it is a factor that can influence the reliability of any localization algorithm founded on RSS. Several algorithms effectively reduce the variance of RSS to improve localization or accuracy performance. Our proposed algorithm essentially avoids this pitfall and consequently, its high adaptability in the face of erratic radio signal. Using 3 anchors in close proximity of each other, we are able to establish that RSS can be used as reliable indicator for localization with an acceptable degree of accuracy. Inherent in this concept, is the ability for each prospective anchor to validate (guarantee) the position or the proximity of the other 2 anchors involved in the localization and vice versa. This procedure ensures that the uncertainties of radio signals due to multipath effects in enclosed environments are minimized. A major driver of this idea is the implicit topological relationship among sensors due to raw radio signal strength. The algorithm is an area based algorithm; however, it does not trade accuracy for precision (i.e the size of the returned area).

Keywords: anchor nodes, centroid algorithm, communication graph, radio signal strength

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Authors: S. B. Kumbalavati, J. D. Mallapur, K. Y. Bendigeri

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A mobile Ad-hoc network (MANET) is a multihop wireless network where nodes communicate each other without any pre-deployed infrastructure. There is no central administrating unit. Hence, MANET is generally prone to many of the attacks. These attacks may alter, release or deny data. These attacks are nothing but intrusions. Intrusion is a set of actions that attempts to compromise integrity, confidentiality and availability of resources. A major issue in the design and operation of ad-hoc network is sharing the common spectrum or common channel bandwidth among all the nodes. We are performing intrusion detection using game theory approach. Game theory is a mathematical tool for analysing problems of competition and negotiation among the players in any field like marketing, e-commerce and networking. In this paper mathematical model is developed using game theory approach and intruders are detected and removed. Bandwidth utilization is estimated and comparison is made between bandwidth utilization with intrusion detection technique and without intrusion detection technique. Percentage of intruders and efficiency of the network is analysed.

Keywords: ad-hoc network, IDS, game theory, sensor networks

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Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: Osayande Pascal Omondiagbe, Sherlock a Licorish

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Stack Overflow is a popular community question and answer portal which is used by practitioners to solve technology-related challenges during software development. Previous studies have shown that this forum is becoming a substitute for official software programming languages documentation. While tools have looked to aid developers by presenting interfaces to explore Stack Overflow, developers often face challenges searching through many possible answers to their questions, and this extends the development time. To this end, researchers have provided ways of predicting acceptable Stack Overflow answers by using various modeling techniques. However, less interest is dedicated to examining the performance and quality of typically used modeling methods, and especially in relation to models’ and features’ complexity. Such insights could be of practical significance to the many practitioners that use Stack Overflow. This study examines the performance and quality of various modeling methods that are used for predicting acceptable answers on Stack Overflow, drawn from 2014, 2015 and 2016. Our findings reveal significant differences in models’ performance and quality given the type of features and complexity of models used. Researchers examining classifiers’ performance and quality and features’ complexity may leverage these findings in selecting suitable techniques when developing prediction models.

Keywords: feature selection, modeling and prediction, neural network, random forest, stack overflow

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Authors: Masoud Mohammadpour, Nima Yazdian, Radovan Kovacevic

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The Oil and Gas industries are vigorously looking for new ways to increase the efficiency of their pipeline constructions. Besides the other approaches, implementing of new welding methods for joining pipes can be the best candidate on this regard. Hybrid Laser Arc Welding (HLAW) with the capabilities of high welding speed, deep penetration, and excellent gap bridging ability can be a possible alternative method in pipeline girth welding. This paper investigates the feasibility of applying the HLAW to join ASTM A106-B as the mostly used piping material for transporting high-temperature and high-pressure fluids and gases. The experiments were carried out on six-inch diameter pipes with the wall thickness of 10mm. AWS ER 70 S6 filler wire with diameter of 1.2mm was employed. Relating to this welding procedure, characterization of welded samples such as hardness, tensile testing and Charpy V-notch testing were performed and the results will be reported in this paper. In order to have better understanding about the thermal history and the microstructural alterations caused by the welding heat cycle, a comprehensive Finite Element (FE) model was also conducted. The obtained results have shown that the Gas Metal Arc Welding (GMAW) procedure with the minimum number of 5 passes to complete the wall thickness, was reduced to only single pass by using the HLAW process with the welding time less than 15s.

Keywords: finite element modeling, high-temperature service, hybrid laser/arc welding, welding pipes

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Authors: Muhammad Aliyu

Abstract:

Significant improvement in Information and Communication Technology (ICT) and the enforced global competition are revolutionizing the way knowledge is managed and the way organizations compete. The emergence of new organizations calls for a new way to fraternize knowledge, which is known as 'knowledge fraternization.' In this modern economy, it is the knowledge if properly managed that can harness the organization's competitive advantage. This competitive advantage is realized through the full utilization of information and data coupled with the harnessing of people’s skills and ideas as well as their commitment and motivations, which can be accomplished through socializing the knowledge management processes. A fraternize network for knowledge management is a web-based system designed using PHP that is Dreamweaver web development tool, with the help of CS4 Adobe Dreamweaver as the PHP code Editor that supports the use of Cascadian Style Sheet (CSS), MySQL with Xamp, Php My Admin (Version 3.4.7) localhost server via TCP/IP for containing the databases of the system to support this in a distributed way, spreading the workload over the whole organization. This paper reviews the technologies and the technology tools to be used in the development of social networks in an organization.

Keywords: Information and Communication Technology (ICT), knowledge, fraternization, social network

Procedia PDF Downloads 396

Authors: Elitsa N. Kolentsova, Dimitar Y. Dimitrov, Petya Cv. Petrova, Georgi V. Avdeev, Diana D. Nihtianova, Krasimir I. Ivanov, Tatyana T. Tabakova

Abstract:

Recently, copper and manganese-containing systems are recognized as active and selective catalysts in many oxidation reactions. The main idea of this study is to obtain more information about γ-Al₂O₃ supported Cu-La catalysts and to evaluate their activity to simultaneous oxidation of CO, CH₃OH and dimethyl ether (DME). The catalysts were synthesized by impregnation of support with a mixed aqueous solution of nitrates of copper, manganese and lanthanum under different conditions. XRD, HRTEM/EDS, TPR and thermal analysis were performed to investigate catalysts’ bulk and surface properties. The texture characteristics were determined by Quantachrome Instruments NOVA 1200e specific surface area and pore analyzer. The catalytic measurements of single compounds oxidation were carried out on continuous flow equipment with a four-channel isothermal stainless steel reactor in a wide temperature range. On the basis of XRD analysis and HRTEM/EDS, it was concluded that the active component of the mixed Cu-Mn-La/γ–alumina catalysts strongly depends on the Cu/Mn molar ratio and consisted of at least four compounds – CuO, La₂O₃, MnO₂ and Cu_1.5Mn_1.5O₄. A homogeneous distribution of the active component on the carrier surface was found. The chemical composition strongly influenced catalytic properties. This influence was quite variable with regards to the different processes.

Keywords: Cu-Mn-La oxide catalysts, carbon oxide, VOCs, deep oxidation

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Authors: Yuqing Chen, Ying Xu, Renfa Li

Abstract:

The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

Procedia PDF Downloads 385

Authors: Carlos Gordon, Patricio Encalada, Henry Lema, Diego Leon, Dennis Chicaiza

Abstract:

The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms.

Keywords: autonomous navigation, machine learning, path planning, robotic operative system, open source computer vision library

Procedia PDF Downloads 178