Search results for: intelligent identification

Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

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Authors: Wiseborn Manfe Danquah, D. Turgay Altilar

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Vehicular Ad-hoc Network (VANET) is an integral component of Intelligent Transport Systems (ITS) that has enjoyed a lot of attention from the research community and the automotive industry. This is mainly due to the opportunities and challenges it presents. Vehicular Ad-hoc Network being a class of Mobile Ad-hoc Networks (MANET) has all the security concerns existing in traditional MANET as well as new security and privacy concerns introduced by the unique vehicular communication environment. This paper provides a survey of the possible attacks in vehicular environment, as well as security and privacy concerns in VANET. It also provides an insight into the development of a comprehensive cloud framework to provide a more robust and secured communication among vehicular nodes and road side units. Our proposal, a Metropolitan Based Public Interconnected Vehicular Cloud (MIVC) infrastructure seeks to provide a more reliable and secured vehicular communication network.

Keywords: mobile Ad-hoc networks, vehicular ad hoc network, cloud, ITS, road side units (RSU), metropolitan interconnected vehicular cloud (MIVC)

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Authors: Rogerio Vieira Dos Reis

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Countries like Brazil that despite fighting hard against corruption are not improving their corruption perception, especially due to systemic political corruption, should review their corruption prevention strategies. This thesis brings a case study based on an alternative way of preventing corruption: addressing the corruption drivers in public policies that lead to poor economic performance. After discussing the Brazilian industrial policies adopted recently, especially the measures towards the automotive sector, two corruption issues in this sector are analyzed: facilitating payment for fiscal benefits and buying the extension of fiscal benefits. In-depth interviews conducted with a policymaker and an executive of the automobile sector provide insights for identifying three main corruption drivers: excessive and unnecessary bureaucracy, a complex tax system and the existence of a closed market without setting performance requirements to be achieved by the benefited firms. Both the identification of the drivers of successful industrial policies and the proposal of anti-corruption strategies to ensure developmental outcomes are based on the economic perspective of industrial policy advocated by developmental authors and on the successful South Korean economic development experience. Structural anti-corruption measures include tax reform, the regulation of lobbying and legislation to allow corporate political contribution. Besides improving policymakers’ technical capabilities, measures at the ministry level include redesigning the automotive regimes as long-term policies focused on national investment with simple and clear rules and making fiscal benefits conditional upon performance targets focused on suppliers. This case study is of broader interest because it recommends the importance of adapting performance audits conducted by anti-corruption agencies, to focus not only on the delivery of public services, but also on the identification of potentially highly damaging corruption drivers in public policies that grant fiscal benefits to achieve developmental outcomes.

Keywords: Brazilian automotive sector, corruption, economic development, industrial policy, Inovar-Auto

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Authors: Agria Rhamdhan

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WhatsApp as the most popular mobile messaging app has been used as evidence in many criminal cases. As the use of mobile messages generates large amounts of data, forensic investigation faces the challenge of large data problems. The hardest part of finding this important evidence is because current practice utilizes tools and technique that require manual analysis to check all messages. That way, analyze large sets of mobile messaging data will take a lot of time and effort. Our work offers methodologies based on forensic triage to reduce large data to manageable sets resulting easier to do detailed reviews, then show the results through interactive visualization to show important term, entities and relationship through intelligent ranking using Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) Model. By implementing this methodology, investigators can improve investigation processing time and result's accuracy.

Keywords: forensics, triage, visualization, WhatsApp

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Authors: Altayeb Abdalla Ahmed

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Skeletal phenotype is a product of a balanced interaction between genetics and environmental factors throughout different life stages. Therefore, interlimb proportions are variable between populations. Although interlimb proportion indices have been used in anthropology in assessing the influence of various environmental factors on limbs, an extensive literature review revealed that there is a paucity of published research assessing interlimb part correlations and possibility of reconstruction. Hence, this study aims to assess the relationships between upper and lower limb parts and develop regression formulae to reconstruct the parts from one another. The left upper arm length, ulnar length, wrist breadth, hand length, hand breadth, tibial length, bimalleolar breadth, foot length, and foot breadth of 376 right-handed subjects, comprising 187 males and 189 females (aged 25-35 years), were measured. Initially, the data were analyzed using basic univariate analysis and independent t-tests; then sex-specific simple and multiple linear regression models were used to estimate upper limb parts from lower limb parts and vice-versa. The results of this study indicated significant sexual dimorphism for all variables. The results indicated a significant correlation between the upper and lower limbs parts (p < 0.01). Linear and multiple (stepwise) regression equations were developed to reconstruct the limb parts in the presence of a single or multiple dimension(s) from the other limb. Multiple stepwise regression equations generated better reconstructions than simple equations. These results are significant in forensics as it can aid in identification of multiple isolated limb parts particularly during mass disasters and criminal dismemberment. Although a DNA analysis is the most reliable tool for identification, its usage has multiple limitations in undeveloped countries, e.g., cost, facility availability, and trained personnel. Furthermore, it has important implication in plastic and orthopedic reconstructive surgeries. This study is the only reported study assessing the correlation and prediction capabilities between many of the upper and lower dimensions. The present study demonstrates a significant correlation between the interlimb parts in both sexes, which indicates a possibility to reconstruction using regression equations.

Keywords: anthropometry, correlation, limb, Sudanese

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Authors: Aymen Sassi

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The equipment of vehicles with wireless communication capabilities is expected to be the key to the evolution to next generation intelligent transportation systems (ITS). The IEEE community has been continuously working on the development of an efficient vehicular communication protocol for the enhancement of Wireless Access in Vehicular Environment (WAVE). Vehicular communication systems, called V2X, support vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. The efficiency of such communication systems depends on several factors, among which the surrounding environment and mobility are prominent. Accordingly, this study focuses on the evaluation of the real performance of vehicular communication with special focus on the effects of the real environment and mobility on V2X communication. It starts by identifying the real maximum range that such communication can support and then evaluates V2I and V2V performances. The Arada LocoMate OBU transmission system was used to test and evaluate the impact of the transmission range in V2X communication. The evaluation of V2I and V2V communication takes the real effects of low and high mobility on transmission into account.

Keywords: IEEE 802.11p, V2I, V2X, mobility, PLR, Arada LocoMate OBU, maximum range

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Authors: Aayushi Patidar, Nishant Parihar

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One of the principal causes of failure in existing vehicle brokerage solutions is that they require the introduction of a single trusted third party to whom transport offers and requirements are sent, and which solves the scheduling problem. Advances in planning and scheduling could be utilized to address the scalability issues inherent here, but such refinements do not address the key need to decentralize decision-making. This is not to say that matchmaking of potential transport suppliers to consumers is not essential, but information from such a service should inform rather than determining the transport options for customers. The approach that is proposed, is the use of intelligent commuters that act within the system and to identify options open to users, weighing the evidence for desirability of each option given a model of the user’s priorities, and to drive dialogue among commuters in aiding users to solve their individual (or collective) transport goals. Existing research in commuter support for transport resource management has typically been focused on the provider. Our vision is to explore both the efficient use of limited transport resources and also to support the passengers in the transportation flexibility & integration among various modes in India.

Keywords: flexibility, integration, service design, technology

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Authors: Rachel deCatanzaro, Madeline Austen, Ken Tuininga, Kathy St. Laurent, Christina Rohe

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In Canada, the federal Species at Risk Act (SARA) provides protection for wildlife species at risk and a national legislative framework for the conservation or recovery of species that are listed as endangered, threatened, or special concern under Schedule 1 of SARA. Key aspects of the federal species at risk program include the development of recovery documents (recovery strategies, action plans, and management plans) outlining threats, objectives, and broad strategies or measures for conservation or recovery of the species; the identification and protection of critical habitat for threatened and endangered species; and working with groups and organizations to implement on-the-ground recovery actions. Environment Canada’s progress on the development of recovery documents and on the identification and protection of critical habitat in Ontario will be presented, along with successes and challenges associated with on-the ground implementation of recovery actions. In Ontario, Environment Canada is currently involved in several recovery and monitoring programs for at-risk bird species such as the Loggerhead Shrike, Piping Plover, Golden-winged Warbler and Cerulean Warbler and has provided funding for a wide variety of recovery actions targeting priority species at risk and geographic areas each year through stewardship programs including the Habitat Stewardship Program, Aboriginal Fund for Species at Risk, and the Interdepartmental Recovery Fund. Key research needs relevant to the recovery of species at risk have been identified, and include: surveys and monitoring of population sizes and threats, population viability analyses, and addressing knowledge gaps identified for individual species (e.g., species biology and habitat needs). The engagement of all levels of government, the local and international conservation communities, and the scientific research community plays an important role in the conservation and recovery of species at risk in Ontario– through surveying and monitoring, filling knowledge gaps, conducting public outreach, and restoring, protecting, or managing habitat – and will be critical to the continued success of the federal species at risk program.

Keywords: conservation biology, habitat protection, species at risk, wildlife recovery

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Authors: Smail Tigani, Mohamed Ouzzif

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This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: Halleh Nejadriahi, Kamyar Arab

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Computer-aided design (CAD) tools have been extensively used by the architects for the several decades. It has evolved from being a simple drafting tool to being an intelligent architectural software and a powerful means of communication for architects. CAD plays an essential role in the profession of architecture and is a basic tool for any architectural firm. It is not possible for an architectural firm to compete without taking the advantage of computer software, due to the high demand and competition in the architectural industry. The aim of this study is to evaluate the impacts of CAD on the architectural design process from conceptual level to final product, particularly in architectural practice. It examines the range of benefits of integrating CAD into the industry and discusses the possible defects limiting the architects. Method of this study is qualitatively based on data collected from the professionals’ perspective. The identified benefits and limitations of CAD on the architectural design process will raise the awareness of professionals on the potentials of CAD and proper utilization of that in the industry, which would result in a higher productivity along with a better quality in the architectural offices.

Keywords: architecture, architectural practice, computer aided design (CAD), design process

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Authors: Jularat Chumnaul

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In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

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The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Fathi M. O. Hamed, Salma F. Elkofhaifee

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The aim of this work is to detect geometrical shape objects in an image. In this paper, the object is considered to be as a circle shape. The identification requires find three characteristics, which are number, size, and location of the object. To achieve the goal of this work, this paper presents an algorithm that combines from some of statistical approaches and image analysis techniques. This algorithm has been implemented to arrive at the major objectives in this paper. The algorithm has been evaluated by using simulated data, and yields good results, and then it has been applied to real data.

Keywords: image processing, median filter, projection, scale-space, segmentation, threshold

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Authors: Marisa Almeida AraúJo

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Artificial intelligence (AI) tools are being used (including in the criminal justice system) and becomingincreasingly popular. The many questions that these (future) super-beings pose the neuralgic center is rooted in the (old) problematic between rationality and morality. For instance, if we follow a Kantian perspective in which morality derives from AI, rationality will also surpass man in ethical and moral standards, questioning the nature of mind, the conscience of self and others, and moral. The recognition of superior intelligence in a non-human being puts us in the contingency of having to recognize a pair in a form of new coexistence and social relationship. Just think of the humanoid robot Sophia, capable of reasoning and conversation (and who has been recognized for Saudi citizenship; a fact that symbolically demonstrates our empathy with the being). Machines having a more intelligent mind, and even, eventually, with higher ethical standards to which, in the alluded categorical imperative, we would have to subject ourselves under penalty of contradiction with the universal Kantian law. Recognizing the complex ethical and legal issues and the significant impact on human rights and democratic functioning itself is the goal of our work.

Keywords: ethics, artificial intelligence, legal rules, principles, philosophy

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Adetoba B. Tiwalola, Adedeji W. Oyediran, Yekini N. Asafe, Akinwole A. Kikelomo

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Mechatronics is synergistic integration of mechanical engineering, with electronics and intelligent computer control in the design and manufacturing of industrial products and processes. Automobile (auto car, motor car or car is a wheeled motor vehicle used for transporting passengers, which also carries its own engine or motor) is a mechatronic system which served as major means of transportation around the world. Virtually all community has a need for automobile. This makes automobile issues as related to diagnostic and repair interesting to all communities. Consequent to the diversification of skill in diagnosing automobile faults and approaches in solving some problems and innovation in automobile industry. It is appropriate to say that repair and diagnostic of automobile will be better enhanced if community has opportunity of sharing knowledge and idea globally. This paper discussed the desirable elements in automobile as mechatronics system and present conceptual framework of virtual community model for knowledge sharing among automobile users.

Keywords: automobile, automobile users, knowledge sharing, mechatronics system, virtual community

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Authors: Recep Kesli, Gulşah Asik, Cengiz Demir, Onur Turkyilmaz

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Objective: Acinetobacter baumannii (A. baumannii) can cause health-care associated infections, such as bacteremia, urinary tract and wound infections, endocarditis, meningitis, and pneumonia, particularly in intensive care unit patients. In this study, we aimed to evaluate A. baumannii production in sputum and bronchoalveolar lavage and susceptibilities for antibiotics in a 24 months period. Methods: Between October 2013 and September 2015, Acinetobacter baumannii isolated from respiratory tract speciments were evaluated retrospectively. The strains were isolated from the different intensive care units patients. A. baumannii strains were identified by both the conventional methods and aoutomated identification system -VITEK 2 (bio-Merieux, Marcy l’etoile, France). Antibiotic resistance testing was performed by Kirby-Bauer disc diffusion method according to CLSI criteria. Results: All the ninety isolates included in the study were from respiratory tract specimens. While of all the isolated 90 Acinetobacter baumannii strains were found to be resistant (100%), against ceftriaxone, ceftazidime, ciprofloxacin and piperacillin/ tazobactam, resistance rates against other tested antibiotics found as follows; meropenem 77, 86%, imipenem 75, 83%, trimethoprim-sulfamethoxazole (TMP-STX) 69, 76,6%, gentamicin 51, 56,6% and amikacin 48, 53,3%. Colistin was found as the most effective antibiotic against Acinetobacter baumannii, and there were not found any resistant (0%) strain against colistin. Conclusion: This study demonstrated that the no resistance was found in Acinetobacter baumannii against to colistin. High rates of resistance to carbapenems (imipenem and meropenem) and other tested antibiotics (ceftiaxone, ceftazidime, ciprofloxacine, piperacilline-tazobactam, TMP-STX gentamicin and amikacin) also have remarkable resistance rates. There was a significant relationship between demographic features of patients such as age, undergoing mechanical ventilation, length of hospital stay with resistance rates. High resistance rates against antibiotics require implementation of the infection control program and rational use of antibiotics. In the present study, while there were not found colistin resistance, panresistance were found against to ceftriaxone, ceftazidime, ciprofloxacin and piperacillin/ tazobactam.

Keywords: acinetobacter baumannii, antibiotic resistance, multi drug resistance, intensive care unit

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Authors: Jakub Młoźniak, Adam Szymański, Gabriela Stondzik, Dagny Krankowska, Tomasz Mikuła

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Nontuberculous mycobacteria (NTM) are bacterial species that cause diversely manifesting diseases mainly in immunocompromised patients. In people with HIV, NTM infection is an AIDS-defining disease and usually appears when the lymphocyte T CD4 count is below 50 cells/μl. The usage of antiretroviral therapy has decreased the prevalence of NTM among people with HIV, but the disease can still be observed especially among patients with late HIV diagnosis. Common presence in environment, human colonization, clinical similarity with tuberculosis and slow growth on culture makes NTM especially hard to diagnose. The study aimed to analyze the epidemiology and clinical course of NTM among patients with HIV. This study included patients with NTM and HIV admitted to our department between 2017 and 2023. Medical records of patients were analyzed and data on age, sex, median time from HIV diagnosis to identification of NTM infection, median CD4 count at NTM diagnosis, methods of determining NTM infection, type of species of mycobacteria identified, clinical symptoms and treatment course were gathered. Twenty-four patients (20 men, 4 women) with identified NTM were included in this study. Among them, 20 were HIV late presenters. The patients' median age was 40. The main symptoms which patients presented were fever, weight loss and cough. Pulmonary disease confirmed with positive cultures from sputum/bronchoalveolar lavage was present in 18 patients. M. avium was the most common species identified. M. marinum caused disseminated skin lesions in 1 patient. Out of all, 5 people were not treated for NTM caused by lack of symptoms and suspicion of colonization with mycobacterium. Concomitant tuberculosis was present in 6 patients. The median diagnostic time from HIV to NTM infections was 3.5 months. The median CD4 count at NTM identification was 69.5 cells/μl. Median NTM treatment time was 16 months but 7 patients haven’t finished their treatment yet. The most commonly used medications were ethambutol and clarithromycin. Among analyzed patients, 4 of them have died. NTM infections are still an important disease among patients who are HIV late presenters. This disease should be taken into consideration during the differential diagnosis of fever, weight loss and cough in people with HIV with lymphocyte T CD4 count <100 cells/μl. Presence of tuberculosis does not exclude nontuberculous mycobacterium coinfection.

Keywords: mycobacteriosis, HIV, late presenter, epidemiology

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Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Muhammad Shahid Shah, Akram Maqbool, Samina Ashraf

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Since start of this century, world has adopted inclusion as a trend in special education. To meet the challenges of inclusion response, the Punjab government has developed a progressive policy to implement inclusive education. The objectives of this research were to analyze the administration and implementation process by consideration on the management, student’s admission process, screening and assessment, adaptations in curriculum and instruction along with an evaluation, government and nonprofit organizations support. The sample consisted of 50 schools both public and private with a total of 3000 students, 9 percent of which (270) were students with disabilities. Among all the students with disabilities, 63 percent (170) were male and 37 percent (100) were female. The concluded remarks regarding management revealed that a large number of inclusive schools was lacking in terms of developing a certain model for inclusion, including the managerial breakup of staff, the involvement of stakeholders, and conducted frequent meetings. Many of schools are not able to restructure their school organizations due to lack of financial resources, consultations, and backup. As for as student’s admission/identification/assessment was concerned, only 12 percent schools applied a selection process regarding student admission, half of which used different procedures for disable candidates. Approximately 5 percent of inclusive schools had modified their curriculum, including a variety of standards. In terms of instruction, 25 percent of inclusive schools reported that they modified their instructional process. Only a few schools, however, provided special equipment for students with visual impairment, physical impairment, speech and hearing problems, students with mild intellectual disabilities, and autism. In a student evaluation, more than 45 percent reported that test items, administration, time allocations, and students’ reports were modified. For the primary board examination conducted by the Education Department of Government of Punjab, this number decreased dramatically. Finally, government and nonprofit organizations support in the forms of funding, coaching, and facilities were mostly provided by provincial governments and by Ghazali Education Trust.

Keywords: inclusion, identification, assessment, funding, facilities, evaluation

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Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: David Pinzauti, Simon De Jaegher, Maria D'Aguano, Manuele Biazzo

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The COVID-19 pandemic has left an indelible mark on global health, leading to a pressing need for understanding the intricate interactions between the virus and the human microbiome. This study focuses on characterizing the nasal microbiota of patients affected by COVID-19, with a specific emphasis on the comparison with unaffected individuals, to shed light on the crucial role of the microbiome in the development of this viral disease. To achieve this objective, Nanopore technology was employed to analyze the bacterial 16s rRNA full-length gene present in nasal swabs collected in Malta between January 2021 and August 2022. A comprehensive dataset consisting of 268 samples (126 SARS-negative samples and 142 SARS-positive samples) was subjected to a comparative analysis using an in-house, custom pipeline. The findings from this study revealed that individuals affected by COVID-19 possess a nasal microbiota that is significantly less diverse, as evidenced by lower α diversity, and is characterized by distinct microbial communities compared to unaffected individuals. The beta diversity analyses were carried out at different taxonomic resolutions. At the phylum level, Bacteroidota was found to be more prevalent in SARS-negative samples, suggesting a potential decrease during the course of viral infection. At the species level, the identification of several specific biomarkers further underscores the critical role of the nasal microbiota in COVID-19 pathogenesis. Notably, species such as Finegoldia magna, Moraxella catarrhalis, and others exhibited relative abundance in SARS-positive samples, potentially serving as significant indicators of the disease. This study presents valuable insights into the relationship between COVID-19 and the nasal microbiota. The identification of distinct microbial communities and potential biomarkers associated with the disease offers promising avenues for further research and therapeutic interventions aimed at enhancing public health outcomes in the context of COVID-19.

Keywords: COVID-19, nasal microbiota, nanopore technology, 16s rRNA gene, biomarkers

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Authors: N. Chougui, R. Larbat

Abstract:

The first economic importance of Opuntia ficus-indica relies on the production of edible fruits. This food transformation generates a large amount of by-products (seeds and peels) in addition to cladodes produced by the plant. Several studies showed the richness of these products with bioactive substances like phenolics that have potential applications. Indeed, phenolics have been associated with protection against oxidation and several biological activities responsible of different pathologies. Consequently, there has been a growing interest in identifying natural antioxidants from plants. This study falls within the framework of the industrial exploitation of by-products of the plant. The study aims to investigate the metabolic profile of three by-products (cladodes, peel seeds) regarding total phenolic content by liquid chromatography coupled to mass spectrometry approach (LC-MSn). The byproducts were first washed, crushed and stored at negative temperature. The total phenolic compounds were then extracted by aqueous-ethanolic solvent in order to be quantified and characterized by LC-MS. According to the results obtained, the peel extract was the richest in phenolic compounds (1512.58 mg GAE/100 g DM) followed by the cladode extract (629.23 GAE/100 g DM) and finally by the seed extract (88.82 GAE/100 g DM) which is mainly used for its oil. The LC-MS analysis revealed diversity in phenolics in the three extracts and allowed the identification of hydroxybenzoic acids, hydroxycinnamic acids and flavonoids. The highest complexity was observed in the seed phenolic composition; more than twenty compounds were detected that belong to acids esters among which three feruloyl sucrose isomers. Sixteen compounds belonging to hydroxybenzoic acids, hydroxycinnamic acids and flavonoids were identified in the peel extract, whereas, only nine compounds were found in the cladode extract. It is interesting to highlight that the phenolic composition of the cladode extract was closer to that of the peel exact. However, from a quantitative viewpoint, the peel extract presented the highest amounts. Piscidic and eucomic acids were the two most concentrated molecules, corresponding to 271.3 and 121.6 mg GAE/ 100g DM respectively. The identified compounds were known to have high antioxidant and antiradical potential with the ability to inhibit lipid peroxidation and to exhibit a wide range of biological and therapeutic properties. The findings highlight the importance of using the Opuntia ficus-indica by-products.

Keywords: characterization, LC-MSn analysis, Opuntia ficus-indica, phenolics

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

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Authors: Mohammad Rajabalinejad

Abstract:

Safety is often seen as a requirement or a performance indicator through the design process, and this does not always result in optimally safe products or systems. This paper suggests integrating the best safety practices with the design process to enrich the exploration experience for designers and add extra values for customers. For this purpose, the commonly practiced safety standards and design methods have been reviewed and their common blocks have been merged forming Safety Cube. Safety Cube combines common blocks for design, hazard identification, risk assessment and risk reduction through an integral approach. An example application presents the use of Safety Cube for design of machinery.

Keywords: safety, safety cube, product, system, machinery, design

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Authors: Abdullah Bajelan, Adel Akbarimajd

Abstract:

Object manipulation techniques in robotics can be categorized in two major groups including manipulation with grasp and manipulation without grasp. The original aim of this paper is to develop an object manipulation method where in addition to being grasp-less, the manipulation task is done in a passive approach. In this method, linear and angular positions of the object are changed and its manipulation path is controlled. The manipulation path is a helix track with constant radius and incline. The method presented in this paper proposes a system which has not the actuator and the active controller. So this system requires a passive mechanical intelligence to convey the object from the status of the source along the specified path to the goal state. This intelligent is created based on utilizing the geometry of the system components. A general set up for the components of the system is considered to satisfy the required conditions. Then after kinematical analysis, detailed dimensions and geometry of the mechanism is obtained. The kinematical results are verified by simulation in ADAMS.

Keywords: mechanical intelligence, object manipulation, passive mechanism, passive non-prehensile manipulation

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Authors: Adama Sow, Abdoulaye Guiss´e, Oumar Niang

Abstract:

To assist medical diagnosis, we propose a federation of several existing and open medical ontologies and terminologies. The goal is to merge the strengths of all these resources to provide clinicians the access to a variety of shared knowledges that can facilitate identification and association of human diseases and all of their available characteristic signs such as symptoms and clinical signs. This work results to an integration model loaded from target known ontologies of the bioportal platform such as DOID, MESH, and SNOMED for diseases selection, SYMP, and CSSO for all existing signs.

Keywords: medical decision, medical ontologies, ontologies integration, linked data, knowledge engineering, e-health system

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

Procedia PDF Downloads 73