Search results for: rapid identification

Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: Priya Arora, Ashutosh Mishra

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Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

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Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: Aditi Chauhan, Neethu S. Mohan

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In the modern era, the advancement and digitalization in technology have taken place during an investigation of crime scene. The rapid enhancement and investigative techniques have changed the mean of identification of suspect. Identification of the person is one of the significant aspects, and personal authentication is the key of security and reliability in society. Since early 90 s, people have relied on comparing handwriting through its class and individual characteristics. But in today’s 21st century we need more reliable means to identify individual through handwriting. An approach employing computer modalities have lately proved itself auspicious enough in exploration of hieroglyphics substantial in investigating the case. Various software’s such as FISH, WRITEON, and PIKASO, CEDAR-FOX SYSTEM identify and verify the associated quantitative measure of the similarity between two samples. The research till date has been confined to identify the authorship of the concerned samples. But prospects associated with the use of computational modalities might help to identify disguised writing, forged handwriting or say altered or modified writing. Considering the applications of such modal, similar work is sure to attract plethora of research in immediate future. It has a promising role in national security too. Documents exchanged among terrorist can also be brought under the radar of surveillance, bringing forth their source of existence.

Keywords: documents, identity, computational system, suspect

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Authors: Shaoying Guo, Yanyun Xu, Meng Zhang, Weiqing Huang

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The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.

Keywords: cross-validation support vector machine, refined com- posite multiscale dispersion entropy, specific emitter identification, transient signal, wireless communication device

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Sello Mokwena, Monyepao Thabang

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In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques.

Keywords: camera network, convolutional neural network topology, person tracking, person re-identification, siamese

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Authors: Ramin Mirzada

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This research investigates the valuation of public transport importance in decreasing congestion and in introduction of bus rapid transit in Kabul city. The main concern and main problem of the Kabul city public transport is traffic congestion. When buses and trams are stuck in traffic jams, it is clear that they fall behind from the schedule and this cause lots of problem for Kabul residence. In this research, the main attention has been given to improve current public transport in Kabul city which Public transport has large share almost 50% share among all mode. The main purpose of this research is to improve public transport system, to examine the demand and the supply of public transport in Kabul city, and to improve public transport system by introducing Bus rapid transit (BRT) system in Kabul city. The data which is used in this research is gathered by Transport Ministry, Kabul Municipality and Japan Cooperation Agency in Afghanistan (JICA). Urban transportation modeling system (UTMS) which is also known as traditional four-step modeling is used as the methodology of this research. The outcome of this research shows that by improving public transport which is local bus system mostly congestion problem of Kabul city become solve, and for those lanes which has the high demand and has more congestion, it is needed to introduce bus rapid transit system.

Keywords: transportation, planning, public transport, bus rapid transit, Kabul, Afghanistan

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Authors: Mengxin Feng, Feng Xu, Zhiyong Lai

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As a typical representative of the countryside, traditional Chinese villages are rich in cultural landscape resources and historical information, but they are still in continuous decline. The problems of people's weak protection awareness and low cultural recognition are still serious, and the protection of cultural heritage is imminent. At the same time, with the rapid development of rural tourism, its cultural value has been explored and paid attention to again. From the perspective of tourists, this study aimed to explore people's perception and identity of cultural landscape resources under the current cultural tourism development background. We selected eleven typical landscape elements of Lingshui Village, a traditional village in Beijing, as research objects and conducted a questionnaire survey with two scales of perception and identity to explore the characteristics of people's perception and identification of landscape elements. We found that there was a strong positive correlation between the perception and identity of each element and that geographical location influenced visitors' overall perception. The perception dimensions scored the highest in location, and the lowest in history and culture, and the identity dimensions scored the highest in meaning and lowest in emotion. We analyzed the impact of visitors' backgrounds on people's perception and identity characteristics and found that age and education were two important factors. The elderly had a higher degree of perceived identity, as the familiarity effect increased their attention. Highly educated tourists had more stringent criteria for perception and identification. The above findings suggest strategies for conserving and optimizing landscape elements in the traditional village to improve the acceptance and recognition of cultural information in traditional villages, which will inject new vitality into the development of traditional villages.

Keywords: traditional village, tourist perception, landscape elements, perception and identity

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Authors: Venkatesh Pulletikurthi, Karthik B. Ariyur, Luciano Castillo

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The system identification from the turbulence wakes will lead to the tactical advantage to prepare and also, to predict the trajectory of the opponents’ movements. A low-order modeling technique, POD, is used to predict the object based on the wake pattern and compared with pre-trained image recognition neural network (NN) to classify the wake patterns into objects. It is demonstrated that low-order modeling, POD, is able to predict the objects better compared to pretrained NN by ~30%.

Keywords: the bluff body wakes, low-order modeling, neural network, system identification

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: S. B. Rathna Kumar, Sandya K. Varudhini, Aparna Ravichandran

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Telugu is a south central Dravidian language spoken in Andhra Pradesh, a southern state of India. The available speech identification tests in Telugu have been developed to determine the communication problems of individuals having a flat frequency hearing loss. These conventional speech audiometric tests would provide redundant information when used on individuals with high-frequency sloping hearing loss because of better hearing sensitivity in the low- and mid-frequency regions. Hence, conventional speech identification tests do not indicate the true nature of the communication problem of individuals with high-frequency sloping hearing loss. It is highly possible that a person with a high-frequency sloping hearing loss may get maximum scores if conventional speech identification tests are used. Hence, there is a need to develop speech identification test materials that are specifically designed to assess the speech identification performance of individuals with high-frequency sloping hearing loss. The present study aimed to develop speech identification test for individuals with high-frequency sloping hearing loss in Telugu. Individuals with high-frequency sloping hearing loss have difficulty in perception of voiceless consonants whose spectral energy is above 1000 Hz. Hence, the word lists constructed with phonemes having mid- and high-frequency spectral energy will estimate speech identification performance better for such individuals. The phonemes /k/, /g/, /c/, /ṭ/ /t/, /p/, /s/, /ś/, /ṣ/ and /h/are preferred for the construction of words as these phonemes have spectral energy distributed in the frequencies above 1000 KHz predominantly. The present study developed two word lists in Telugu (each word list contained 25 words) for evaluating speech identification performance of individuals with high-frequency sloping hearing loss. The performance of individuals with high-frequency sloping hearing loss was evaluated using both conventional and high-frequency word lists under recorded voice condition. The results revealed that the developed word lists were found to be more sensitive in identifying the true nature of the communication problem of individuals with high-frequency sloping hearing loss.

Keywords: speech identification test, high-frequency sloping hearing loss, recorded voice condition, Telugu

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Authors: Liang Hu

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The innovation of refractory high-entropy alloy (RHEA) formed from refractory metals W, Ta, Mo, Nb, Hf, V, and Zr was firstly implemented in 2010 to obtain better strength at high temperature than conventional HEAs based on Al, Co, Cr, Cu, Fe and Ni. Due to the refractory characteristic and high chemical activity at elevated temperature, electrostatic levitation technique has been utilized to fulfill the rapid solidification of RHEA. Several RHEAs consisting W, Ta, Mo, Nb, Zr have been selected to perform the undercooling and rapid solidification by ESL. They are substantially undercooled by up to 0.2TL. The evolution of as-solidified microstructure and component redistribution with undercooling have been investigated by SEM, EBSD, and EPMA analysis. According to the EPMA results of composing elements at different undercooling levels, the chemical distribution relevant to undercooling was also analyzed.

Keywords: chemical distribution, high-entropy alloy, rapid solidification, undercooling

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

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Authors: Chi-Chang Lin, Jian-Rong Wu, Jiun-Yan Chiu

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Myopia, ubiquitous symptom that is necessary to correct the eyesight by optical lens struggles many people for their daily life. Recent years, younger people raise interesting on using contact lens because of its convenience and aesthetics. In clinical, the risk of eye infections increases owing to the behavior of incorrectly using contact lens unsupervised cleaning which raising the infection risk of cornea, named ocular keratitis. In order to overcome the identification needs, new detection or analysis method with rapid and more accurate identification for clinical microorganism is importantly needed. In our study, we take advantage of Raman spectroscopy having unique fingerprint for different functional groups as the distinct and fast examination tool on microorganism. As we know, Raman scatting signals are normally too weak for the detection, especially in biological field. Here, we applied special SERS enhancement substrates to generate higher Raman signals. SERS filter we designed in this article that prepared by deposition of silver nanoparticles directly onto cellulose filter surface and suspension nanoparticles - gold nanostars (AuNSs) also be introduced together to achieve better enhancement for lower concentration analyte (i.e., various bacteria). Research targets also focusing on studying the shape effect of synthetic AuNSs, needle-like surface morphology may possible creates more hot-spot for getting higher SERS enhance ability. We utilized new designed SERS technology to distinguish the bacteria from ocular keratitis under strain level, and specific Raman and SERS fingerprint were grouped under pattern recognition process. We reported a new method combined different SERS substrates can be applied for clinical microorganism detection under strain level with simple, rapid preparation and low cost. Our presenting SERS technology not only shows the great potential for clinical bacteria detection but also can be used for environmental pollution and food safety analysis.

Keywords: bacteria, gold nanostars, Raman spectroscopy surface-enhanced Raman scattering filter

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Rickard Grassman, Carl Cederström

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In this paper, Lacanian theory will be used to illustrate the way discourses interact with the material by way of reifying antagonisms to shape our sense of identities in and around organizations. The ability to ‘sustain the loss’ is, in this view, the common structure here discerned in the very texture of a discourse, which reifies ‘lack’ as an ontological condition into something contingently absent (loss) that the subject hopes to overcome (desire). These fundamental human tendencies of identification are illustrated in the paper by examples drawn from history, cinema, and literature. Turning to a select sample of empirical accounts from a management consultancy firm, it is argued that this ‘sustaining the loss’ operates in discourse to enact identification in an organizational context.

Keywords: Lacan, identification, discourse, desire, loss

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Authors: Aqeela Ashraf, Muhammad Imran, Tahir Yaqub, Muhammad Tayyab, Yung Fu Chang

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For the identification of bovine mastitic pathogen, an economical, rapid and sensitive molecular diagnostic assay is developed by PCR multiplexing of gene and pathogenic species specific DNA sequences. The multiplex PCR assay is developed for detecting nine important bacterial pathogens causing mastitis Worldwide. The bacterial species selected for this study are Streptococcus agalactiae, Streptococcus dysagalactiae, Streptococcus uberis, Staphylococcus aureus, Escherichia coli, Staphylococcus haemolyticus, Staphylococcus chromogenes Mycoplasma bovis and Staphylococcus epidermidis. A single reaction assay was developed and validated by 27 reference strains and further tested on 276 bacterial strains obtained from culturing mastitic milk. The multiplex PCR assay developed here is further evaluated by applying directly on genomic DNA isolated from 200 mastitic milk samples. It is compared with bacterial culturing method and proved to be more sensitive, rapid, economical and can specifically identify 9 bacterial pathogens in a single reaction. It has detected the pathogens in few culture negative mastitic samples. Recognition of disease is the foundation of disease control and prevention. This assay can be very helpful for maintaining the udder health and milk monitoring.

Keywords: multiplex PCR, bacteria, mastitis, milk

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Authors: Namrata Ghosh, Sapan Tiwari

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Public transport service plays an important role in people's transportation needs in urban areas. Bus Rapid Transit System (BRTS) is a transport service that provides passengers with a quick and efficient mode of transport. It is developed by changing the existing infrastructure, vehicles, route, or by developing a new dedicated corridor for the bus route. This dedicated lanes transport passengers to their destination quickly and efficiently and flexible in meeting demand. However, with rapid urbanization and increasing population density in Indian cities, traffic congestion has become a significant issue. In a few Indian cities, the BRTS concept is implemented to address the issue of traffic congestion that eventually resulted in less road congestion. The research aims to provide a literature review on the overall outlook of the BRTS system and its practical implementation in mass urban transit. First, it reflects a literature review on the concept of the BRTS system in both developed and developing countries. Afterward, comparative analysis of BRTS, hindrances associated with the permanent integrated system, and the need for establishing the Bus Rapid Transit System in Indian cities is demonstrated. The research concludes with some recommendations that could help in improving the loopholes in the existing system.

Keywords: bus rapid transit system(BRTS), dedicated corridor, public transport, traffic congestion

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Authors: Kevlin Govender, Anthony Walker, Glen Bright

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Mass customization is an area of increased importance and the development of rapid tooling applications is pivotal to the success of mass customization. This paper presents a laminated object manufacturing (LOM) process for rapid tooling. The process is termed 3D metal laminate printing and utilizes domestic-grade aluminum foil and epoxy for layered manufacturing. A detailed explanation of the process is presented to produce complex metal laminated composite parts. Aluminum-epoxy composite specimens were manufactured from 0.016mm aluminum and subjected to tensile tests to determine the mechanical properties of the manufactured composite in relation to solid metal specimens. The fracture zone of the specimens was analyzed under scanning electron microscopy (SEM) in order to characterize the fracture mode and study the interfacial bonding of the manufactured laminate specimens.

Keywords: 3D metal laminate printer, aluminum-epoxy composite, laminated object manufacturing, rapid tooling

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: Mehran Nasiri, Ardeshir Poornemat

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The aim of this study was to investigate the current and ideal situations of the process of talent identification in Iranian football from the point of view of Iranian instructors of the Asian Football Confederation (AFC). This research was a descriptive-analytical study; in data collection phase a questionnaire was used, whose face validity was confirmed by experts of Physical Education and Sports Science. The reliability of questionnaire was estimated through the use of Cronbach's alpha method (0.91). This study involved 122 participants of Iranian instructors of the AFC who were selected based on stratified random sampling method. Descriptive statistics were used to describe the variables and inferential statistics (Chi-square) were used to test the hypotheses of the study at significant level (p ≤ 0.05). The results of Chi-square test related to the point of view of Iranian instructors of the AFC showed that the grass-roots scientific method was the best way to identify football players (0.001), less than 10 years old were the best ages for talent identification (0.001), the Football Federation was revealed to be the most important organization in talent identification (0.002), clubs were shown to be the most important institution in developing talents (0.001), trained scouts of Football Federation were demonstrated to be the best and most appropriate group for talent identification (0.001), and being referred by the football academy coaches was shown to be the best way to attract talented football players in Iran (0.001). It was also found that there was a huge difference between the current and ideal situation of the process of talent identification in Iranian football from the point of view of Iranian instructors of the AFC. Hence, it is recommended that the policy makers of talent identification for Iranian football provide a comprehensive, clear and systematic model of talent identification and development processes for the clubs and football teams, so that the talent identification process helps to nurture football talents more efficiently.

Keywords: current situation, talent finding, ideal situation, instructors (AFC)

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Maha Alaoui Ismaili, Bouchta Saidi, Mohamed Zahar, Abed Hamama

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112 lactic isolates were obtained from 15 samples of camel raw milk produced in Laayoune Boujdour Sakia-El Hamra region (South of Morocco). The main objective was the identification of species of lactic flora belonging to Lactococcus, Lactobacillus and Leuconostoc. Data obtained showed predominance of cocci among lactic isolates (86.6%) while lactic rods represented only 13.4%. With regard to genera identified, Enterococcus was the mostly found out (53.57%), followed by Lactococcus (28.57%), Lactobacillus (13.4%) and Leuconostoc (4.4 %). Identification of the lactic isolates according to their morphological, physiological, and biochemical characteristics led to differentiating 11 species with Lactococcus lactis ssp lactis biovar diacetylactis being the mostly encountered (24.1%) followed by Lactobacillus brevis (3.57%), Lactobacillus plantarum (3.57%), Lactobacillus delbrueckii subsp lactis (3.57%) and Lactococcus lactis subsp cremoris (2.67%).

Keywords: raw camel milk, south of morocco, lactic acid bacteria, identification

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Ziwei Chen, Yujiang Gao

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The rapid urbanization has caused the massive physical space in Chinese cities to be in a state of duplication and dislocation through the rapid development, forming many daily spaces that cannot be standardized, typed, and identified, such as illegal construction. This phenomenon is known as urban spatial redundancy and is often excluded from mainstream architectural discussions because of its 'remaining' and 'excessive' derogatory label. In recent years, some practice architects have begun to pay attention to this phenomenon and tried to tap the value behind it. In this context, the author takes the redundancy phenomenon of resident space as the research object and explores the inspiration to the urban architectural renewal and the innovative residential area model, based on the urban survey of redundant living space in Hongkou District of Shanghai. On this basis, it shows that the changes accumulated in the long-term use of the building can be re-applied to the goals before the design, which is an important link and significance of the existence of an architecture.

Keywords: rapid urbanization, living space redundancy, architectural renewal, residential area model

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Authors: Silvius Stanciu

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Romanian food products sold on European markets have been accused of several non-conformities of quality and safety. Most products incriminated last period were those of animal origin, especially meat and meat products. The study proposed an analysis of the notifications made by network members through Rapid Alert System for Food and Feed on products originating in Romania. As a source of information, the Rapid Alert System portal and the official communications of the National Sanitary Veterinary and Food Safety Authority were used. The research results showed that nearly a quarter of network notifications were rejected and were withdrawn by the European Authority. Although national authorities present these issues as success stories of national quality policies, the large number of notifications related to the volume of exported products is worrying. The paper is of practical and applicative importance for both the business environment and the academic environment, laying the basis for a wider research on the quality differences between Romanian and imported products.

Keywords: food, quality, RASFF, Rapid Alert System for Food and Feed, Romania

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Authors: N. Jinesh, K. Shankar

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This article presents a method of using the one dimensional piezo-electric patch on beam model for structural identification. A hybrid element constituted of one dimensional beam element and a PZT sensor is used with reduced material properties. This model is convenient and simple for identification of beams. Accuracy of this element is first verified against a corresponding 3D finite element model (FEM). The structural identification is carried out as an inverse problem whereby parameters are identified by minimizing the deviation between the predicted and measured voltage response of the patch, when subjected to excitation. A non-classical optimization algorithm Particle Swarm Optimization is used to minimize this objective function. The signals are polluted with 5% Gaussian noise to simulate experimental noise. The proposed method is applied on beam structure and identified parameters are stiffness and damping. The model is also validated experimentally.

Keywords: inverse problem, particle swarm optimization, PZT patches, structural identification

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Authors: Bharti Jain, Rajeev Jain, Abuzar Kabir, Torki Zughaibi, Shweta Sharma

Abstract:

Drug-facilitated crimes (DFCs) entail the use of a single drug or a mixture of drugs to render a victim unable. Traditionally, biological samples have been gathered from victims and conducted analysis to establish evidence of drug administration. Nevertheless, the rapid metabolism of various drugs and delays in analysis can impede the identification of such substances. For this, the present article describes a rapid, sustainable, highly efficient and miniaturized protocol for the identification and quantification of three sedative-hypnotic drugs, namely diazepam, chlordiazepoxide and ketamine in alcoholic beverages and complex food samples (cream of biscuit, flavored milk, juice, cake, tea, sweets and chocolate). The methodology involves utilizing fabric phase sorptive extraction (FPSE) to extract diazepam (DZ), chlordiazepoxide (CDP), and ketamine (KET). Subsequently, the extracted samples are subjected to analysis using gas chromatography-mass spectrometry (GC-MS). Several parameters, including the type of membrane, pH, agitation time and speed, ionic strength, sample volume, elution volume and time, and type of elution solvent, were screened and thoroughly optimized. Sol-gel Carbowax 20M (CW-20M) has demonstrated the most effective extraction efficiency for the target analytes among all evaluated membranes. Under optimal conditions, the method displayed linearity within the range of 0.3–10 µg mL–¹ (or µg g–¹), exhibiting a coefficient of determination (R2) ranging from 0.996–0.999. The limits of detection (LODs) and limits of quantification (LOQs) for liquid samples range between 0.020-0.069 µg mL-¹ and 0.066-0.22 µg mL-¹, respectively. Correspondingly, the LODs for solid samples ranged from 0.056-0.090 µg g-¹, while the LOQs ranged from 0.18-0.29 µg g-¹. Notably, the method showcased better precision, with repeatability and reproducibility both below 5% and 10%, respectively. Furthermore, the FPSE-GC-MS method proved effective in determining diazepam (DZ) in forensic food samples connected to drug-facilitated crimes (DFCs). Additionally, the proposed method underwent evaluation for its whiteness using the RGB12 algorithm.

Keywords: drug facilitated crime, fabric phase sorptive extraction, food forensics, white analytical chemistry

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