Search results for: Deep Jyoti Singh

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

Procedia PDF Downloads 51

Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

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Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

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Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Zhuang Hou, Xiaolei Cao

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The general public and medical professionals recognized the importance of accurately measuring and storing blood oxygen levels and heart rate during the COVID-19 pandemic. The demand for accurate contactless devices was motivated by the need for cross-infection reduction and the shortage of conventional oximeters, partially due to the global supply chain issue. This paper evaluated a contactless mini program HealthyPai’s heart rate (HR) and oxygen saturation (SpO2) measurements compared with other wearable devices. In the HR study of 185 samples (81 in the laboratory environment, 104 in the real-life environment), the mean absolute error (MAE) ± standard deviation was 1.4827 ± 1.7452 in the lab, 6.9231 ± 5.6426 in the real-life setting. In the SpO2 study of 24 samples, the MAE ± standard deviation of the measurement was 1.0375 ± 0.7745. Our results validated that HealthyPai utilizing the Integrated Image Deep Learning Solution (IIDLS) framework, can accurately measure HR and SpO2, providing the test quality at least comparable to other FDA-approved wearable devices in the market and surpassing the consumer-grade and research-grade wearable standards.

Keywords: remote photoplethysmography, heart rate, oxygen saturation, contactless measurement, mini program

Procedia PDF Downloads 135

Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: Zehra Aytac, Nurdilek Gulmezoglu

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Safflower has been grown for centuries for many purposes worldwide. Especially it is important for the orange-red dye from its petal and for its high-quality oil obtained from the seeds. The crop is high adaptable to areas with insufficient rainfall and poor soil conditions. The plant has a deep taproot that can draw moisture and plant nutrients from deep to the subsoil. The research was carried out to study the nitrogen (N) uptake of different safflower cultivars and lines at different stages of growth and different plant parts in the experimental field of Faculty of Agriculture, Eskişehir Osmangazi University under semi-arid conditions. Different safflower cultivars and lines of varied origins were used as the material. The cultivars and lines were planted in a Randomized Complete Block Design with three replications. Two different growth stages (flowering and harvest) and three different plant parts (head, stem+leaf and seed) were determined. The nitrogen concentration of different plant parts was determined by the Kjeldahl method. Statistical analysis were performed by analysis of variance for each growth stage and plant parts taking a level of p < 0.05 and p < 0.01 as significant according to the LSD test. As a result, N concentration showed significant differences among different plant parts and different growth stages for different safflower genotypes of varied origins.

Keywords: Carthamus tinctorius L., growth stages, head N, leaf N, N uptake, seed N, Safflower

Procedia PDF Downloads 224

Authors: Harison Masih, Jyotsna Kiran Peter, Sundara Singh, Geetha Singh

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The present investigation deals with diversity of dermatophytes and keratinophilic fungi from different tourist spots such as Agra Fort, Akbar tomb, It-Mat-Ud-Daulah, Mariam tomb, Radha Swami Bagh, and Taj Mahal of Agra City. These fungi are medically important which causes various infections and diseases in humans and animals. The main reservoir of these pathogens are the keratinous substances that increases due to birds and animal activities in the vicinity of monuments, where thousands (5413266) annual visitors from all over the world are visiting. The soil samples were subjected to isolate the pathogenic fungi through bait technique (buffalo skin, chicken feathers, human hair and goat tail hair). Baits were spread over the soil samples and incubated at room temperature for 30-35 days and pure culture isolates were maintained in SDA medium, stored at 4°C. Highest number of visitors were (3906453) from Taj Mahal, minimum 10785 at Mariam tomb annually, the total 271 isolates were encountered from soil samples out of these 18 genera and 38 species were found in different season. Highest incidence was 4.79% frequency shown by Chrysosporium keratinophilum while least 738% frequency occurrence by Trichophyton simii in soil samples. From the present study it was concluded that the incidence of pathogenic fungal isolates were the common in tourists soil that are etiological agents of superficial mycosis. Thus, both human and animal activity seemed to play an important role in occurrence and distribution of keratinophilic and related dermatophytes at various tourist places of Agra city.

Keywords: dermatophytic fungal diversity, bait technique, visitors at tourist spots, human and animal activities, soil samples

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Authors: Sanjida Akter, Ambali Alade Odebowale, Andrey E. Miroshnichenko, Haroldo T. Hattori

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Photodetectors (PDs) play a pivotal role in optoelectronics and optical devices, serving as fundamental components that convert light signals into electrical signals. As the field progresses, the integration of advanced materials with unique optical properties has become a focal point, paving the way for the innovation of novel PDs. This study delves into the exploration of a cutting-edge photodetector designed for deep and near ultraviolet (UV) applications. The photodetector is constructed with a composite of Zirconium Boride (ZrB2) and Chromium (Cr) alloy, deposited onto a 6H nitrogen-doped silicon carbide substrate. The determination of the optimal alloy thickness is achieved through Finite-Difference Time-Domain (FDTD) simulation, and the synthesis of the alloy is accomplished using radio frequency (RF) sputtering. Remarkably, the resulting photodetector exhibits an exceptional responsivity of 3.5 A/W under an applied voltage of -2 V, at wavelengths of 405 nm and 280 nm. This heterostructure not only exemplifies high performance but also provides a versatile platform for the development of near UV photodetectors capable of operating effectively in challenging conditions, such as environments characterized by high power and elevated temperatures. This study contributes to the expanding landscape of photodetector technology, offering a promising avenue for the advancement of optoelectronic devices in demanding applications.

Keywords: responsivity, silicon carbide, ultraviolet photodetector, zirconium boride

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Authors: Rahbar Ali, Nitin Sharma, Dharmendra Singh, R. P. Singh, S. Muralithar, M. Afzal Ansari

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Heavy-ion-induced reactions on intermediate-mass targets are inherently complex, particularly at low energy levels. The study of these nuclear reactions, especially complete and incomplete fusion reactions, is of utmost importance to nuclear physicists. Researchers have demonstrated interest in exploring the mechanisms of nuclear reactions using heavy-ion beams at energies below 10 MeV/nucleon. In this study, the reaction mechanism of ¹⁶O⁷+ projectiles incident on a ¹⁵⁶Gd target at beam energies ranging from 4 to 7 MeV/nucleon was investigated. To gain a comprehensive understanding of the underlying processes, the excitation functions of evaporation residues produced via complete fusion (CF) and/or incomplete fusion (ICF) were measured. The evaporation residues were populated through xn/pxn and αxn/αpxn emission channels. The measured cross-sections of these residues were compared with the predictions of the statistical model codes PACE-4 and EMPIRE. The measured excitation functions of reaction residues populated through xn and pxn channels are in good agreement with the predictions of the statistical model code PACE4 and EMPIRE. This confirms that the production of these residues is solely due to the CF process. However, a significant enhancement was observed in the measured cross-sections of residues populated through α-emitting channels compared to theoretical predictions. This enhancement in the cross sections for α-emitting channels is ascribed to the ICF processes. The fusion cross-section data were also analyzed within the universal fusion function (UFF) and universal reaction function (URF) approach. The observed fusion suppression is primarily attributed to the breakup of the projectile. The ICF contribution in the reaction is dependent on projectile energy, mass asymmetry of the system and deformation of the target.

Keywords: nuclear reactions, above barrier reactions, evaporation residues, universal fusion function

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Authors: Sakshi Singh, Shampa Chakraverty

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One of the essential characteristics of Blockchain is the ability to validate the integrity of new transactions added to the Blockchain. Moreover, one of the essential consensus algorithms, Proof of Work, performs this job. In this work, we implemented the Proof of Work consensus method on the block formed by performing the transaction using Ganache. The primary goal of this implementation is to understand the process and record how Proof of Work works in reality on newly created blocks.

Keywords: proof of work, blockchain, ganache, smart contract

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Authors: Gaurav Kumar Sinha

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The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

Procedia PDF Downloads 36

Authors: Bilal Murtaza, Xiaoyu Li, Liming Dong, Muhammad Kashif Saleemi, Gen Li, Bowen Jin, Lili Wang, Yongping Xu

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Fusarium spp. produce numerous mycotoxins, such as zearalenone (ZEN), deoxynivalenol (DON), and its acetylated compounds, 3-acetyl-deoxynivalenol (3-ADON) and 15-acetyl-deoxynivalenol (15-ADON) (15-ADON). In a co-culture system, the soil-derived Escherichia marmotae strain degrades ZEN and DON into 3-keto-DON and DOM-1 via enzymatic deep-oxidation. When pure mycotoxins were subjected to Escherichia marmotae in culture flasks, degradation, and detoxification were also attained. DON and ZEN concentrations, ambient pH, incubation temperatures, bacterium concentrations, and the impact of acid treatment on degradation were all evaluated. The results of the ELISA and high-performance liquid chromatography-electrospray ionization-high resolution mass spectrometry (HPLC-ESI-HRMS) tests demonstrated that the concentration of mycotoxins exposed to Escherichia marmotae was significantly lower than the control. ZEN levels were reduced by 43.9%, while zearalenone sulfate ([M/z 397.1052 C18H21O8S1) was discovered as a derivative of ZEN converted by microbes to a less toxic molecule. Furthermore, Escherichia marmotae appeared to metabolize DON 35.10% into less toxic derivatives (DOM-1 at m/z 281 of [DON - O]+ and 3-keto-DON at m/z 295 of [DON - 2H]+). These results show that Escherichia marmotae can reduce Fusarium mycotoxins production, degrade pure mycotoxins, and convert them to less harmful compounds, opening up new possibilities for study and innovation in mycotoxin detoxification.

Keywords: mycotoxins, zearalenone, deoxynivalenol, bacterial degradation

Procedia PDF Downloads 100

Authors: Reva Sharan Thakur, Mrinalini Tiwari, Jyoti das

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Cerebral malaria-associated over expression of pro-inflammatory cytokines and chemokines ultimately results in the up-regulation of adhesion molecules in the brain endothelium leading to sequestration of mature parasitized RBCs in the brain. The high-parasitic load subsequently results in increased mortality or development of neurological symptoms within a week of infection. Studies in the human and experimental cerebral malaria have implicated the breakdown of the integrity of blood-brain barrier during the lethal course of infection, cerebral dysfunction, and fatal organ pathologies that result in multi-organ failure. In the present study, using Plasmodium berghei Anka as a mouse model and in vitro conditions, we have investigated the effect of MSCs to attenuate cerebral malaria pathogenesis by diminishing the effect of inflammation altered organ morphology, reduced parasitemia, and increased survival of the mice. MSCs are also validated for their role in preventing BBB dysfunction and reducing malarial toxins. It was observed that administration of MSCs significantly reduced parasitemia and increased survival in Pb A infected mice. It was further demonstrated that MSCs play a significant role in reversing neurological complexities associated with cerebral malaria. Infusion of MSCs in infected mice decreased hemozoin deposition; oedema, and haemorrhagic lesions in vascular organs. MSCs administration also preserved the integrity of the blood-brain barrier and reduced neural inflammation. Taken together, our results demonstrate the potential of MSCs as an emerging anti-malarial candidate.

Keywords: cerebral malaria, mesenchymal stem cells, erythropoesis, cell death

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Authors: Shenlun Chen, Leonard Wee

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Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

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Authors: Kalpana Sharma, Jyoti Mann

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The study was designed towards institutional analysis for a clear understanding of the process involved in functioning and layout of determinants influencing physical education teacher’s education program in India. This further can be recommended for selection of parameters for creating sector benchmarking for physical education teachers training institutions across India. 165 stakeholders involving students, teachers, parents, administrators were surveyed from the identified seven institutions and universities from different states of India. They were surveyed on the basis of seven broad parameters which were associated with the post graduate physical education program in India. A physical education program assessment tool of 52 items was designed to administer it among the stakeholders selected for the survey. An item analysis of the contents was concluded through the review process from selected experts working in higher education with experience in teacher training program in physical education. The data was collected from the stakeholders of the selected institutions through Physical Education Program Assessment Tool (PEPAT). The hypothesis that PE teacher education program is independent of physical education institutions was significant. The study directed a need towards robust admission process emphasizing on identification, selection of potential candidates and quality control of intake with the scientific process developed according to the Indian education policies and academic structure. The results revealed that the universities do not have similar functional and delivery process related to the physical education teacher training program. The study reflects towards the need for physical education universities and institutions to identify the best practices to be followed regarding the functioning of delivery of physical education programs at various institutions through strategic management studies on the identified parameters before establishing strict standards and norms for achieving excellence in physical education in India.

Keywords: assessment, benchmarking, curriculum, physical education, teacher education

Procedia PDF Downloads 561

Authors: Sihem Salmi Laouar, Khoudair Chabane, Rabah Laouar, Adrian J. Boyce et Anthony E. Fallick

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The solid mass of Essouabaa belongs paléogéography to the field téthysian and belonged to the area of the Mounts of Mellègue. This area was not saved by the oceanic-2 event anoxic (EAO-2) which was announced, over one short period, around the limit cénomanian-turonian. In the solid mass of Essouabba, the dominant sediments, pertaining to this period, are generally fine, dark, laminated and sometimes rolled deposits. They contain a rather rich planktonic microfaune, pyrite, and grains of phosphate, thus translating an environment rather deep and reducing rather deep and reducing. For targeting well the passage Cénomanian-Turonian (C-T) in the solid mass of Essouabaa, of the studies lithological and biostratigraphic were combined with the data of the isotopic analyses carbon and oxygen like with the contents of CaCO3. The got results indicate that this passage is marked by a biological event translated by the appearance of the "filaments" like by a positive excursion of the δ13C and δ18O. The cénomanian-turonian passage in the solid mass of Essouabaa represents a good example where during the oceanic event anoxic a facies with organic matter with contents of COT which can reach 1.36%. C E massive presents biostratigraphic and isotopic similarities with those obtained as well in the areas bordering (ex: Tunisia and Morocco) that throughout the world.

Keywords: limit cénomanian-turonian (C-T), COT, filaments, event anoxic 2 (EAO-2), stable isotopes, mounts of Mellègue, Algeria

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

Procedia PDF Downloads 108

Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

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As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

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Authors: H. Selcuk Halkacı, Mevlüt Türköz, Ekrem Öztürk, Murat Dilmec

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Various attempts concerning the low formability issue for lightweight materials like aluminium and magnesium alloys are being investigated in many studies. Advanced forming processes such as hydroforming is one of these attempts. In last decades sheet hydroforming process has an increasing interest, particularly in the automotive and aerospace industries. This process has many advantages such as enhanced formability, the capability to form complex parts, higher dimensional accuracy and surface quality, reduction of tool costs and reduced die wear compared to the conventional sheet metal forming processes. There are two types of sheet hydroforming. One of them is hydromechanical deep drawing (HDD) that is a special drawing process in which pressurized fluid medium is used instead of one of the die half compared to the conventional deep drawing (CDD) process. Another one is sheet hydroforming with die (SHF-D) in which blank is formed with the act of fluid pressure and it takes the shape of die half. In this study, transition of cup height according to cup diameter between the processes was determined by performing simulation of the processes in Finite Element Analysis. Firstly SHF-D process was simulated for 40 mm cup diameter at different cup heights chancing from 10 mm to 30 mm and the cup height to diameter ratio value in which it is not possible to obtain a successful forming was determined. Then the same ratio was checked for a different cup diameter of 60 mm. Then thickness distributions of the cups formed by SHF-D and HDD processes were compared for the cup heights. Consequently, it was found that the thickness distribution in HDD process in the analyses was more uniform.

Keywords: finite element analysis, HDD, hydroforming sheet metal forming, SHF-D

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Authors: Krasimir Ivanov, Elitsa Kolentsova, Dimitar Dimitrov, Petya Petrova, Tatyana Tabakova

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This work studies the effect of chemical composition on the activity and selectivity of γ–alumina supported CuO/ MnO2/Cr2O3 catalysts toward deep oxidation of CO, dimethyl ether (DME) and methanol. The catalysts were prepared by impregnation of the support with an aqueous solution of copper nitrate, manganese nitrate and CrO3 under different conditions. Thermal, XRD and TPR analysis were performed. The catalytic measurements of single compounds oxidation were carried out on continuous flow equipment with a four-channel isothermal stainless steel reactor. Flow-line equipment with an adiabatic reactor for simultaneous oxidation of all compounds under the conditions that mimic closely the industrial ones was used. The reactant and product gases were analyzed by means of on-line gas chromatographs. On the basis of XRD analysis it can be concluded that the active component of the mixed Cu-Mn-Cr/γ–alumina catalysts consists of at least six compounds – CuO, Cr2O3, MnO2, Cu1.5Mn1.5O4, Cu1.5Cr1.5O4 and CuCr2O4, depending on the Cu/Mn/Cr molar ratio. Chemical composition strongly influences catalytic properties, this influence being quite variable with regards to the different processes. The rate of CO oxidation rapidly decrease with increasing of chromium content in the active component while for the DME was observed the reverse trend. It was concluded that the best compromise are the catalysts with Cu/(Mn + Cr) molar ratio 1:5 and Mn/Cr molar ratio from 1:3 to 1:4.

Keywords: Cu-Mn-Cr oxide catalysts, volatile organic compounds, deep oxidation, dimethyl ether (DME)

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Authors: Vathsala Kaluarachchi, Oshani Wimalarathna, Charith Vandebona, Gayani Chandrarathna, Lakmal Rupasinghe, Windhya Rankothge

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It is the fundamental function of a monitoring system to allow users to collect and process data. A worldwide threat, the corona outbreak has wreaked havoc in Sri Lanka, and the situation has gotten out of hand. Since the epidemic, the Sri Lankan government has been unable to establish a systematic system for monitoring corona patients and providing emergency care in the event of an outbreak. Most patients have been held at home because of the high number of patients reported in the nation, but they do not yet have access to a functioning medical system. It has resulted in an increase in the number of patients who have been left untreated because of a lack of medical care. The absence of competent medical monitoring is the biggest cause of mortality for many people nowadays, according to our survey. As a result, a smartphone app for analyzing the patient's state and determining whether they should be hospitalized will be developed. Using the data supplied, we are aiming to send an alarm letter or SMS to the hospital once the system recognizes them. Since we know what those patients need and when they need it, we will put up a desktop program at the hospital to monitor their progress. Deep learning, image processing and application development, natural language processing, and blockchain management are some of the components of the research solution. The purpose of this research paper is to introduce a mechanism to connect hospitals and patients even when they are physically apart. Further data security and user-friendliness are enhanced through blockchain and NLP.

Keywords: blockchain, deep learning, NLP, monitoring system

Procedia PDF Downloads 133

Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Anuraj Singh

Abstract:

The complex dynamics is explored in a prey predator system with multiple delays. The predator dynamics is governed by Leslie-Gower scheme. The existence of periodic solutions via Hopf bifurcation with respect to delay parameters is established. To substantiate analytical findings, numerical simulations are performed. The system shows rich dynamic behavior including chaos and limit cycles.

Keywords: chaos, Hopf bifurcation, stability, time delay

Procedia PDF Downloads 326

Authors: Neşe Işık, Şenol Altıok, Galip Devrim Eryılmaz, Aydın durukan, Hasan Özgür Daş

Abstract:

In this study, an active landslide zone affecting the road alignment on the Tortum-Uzundere (Erzurum/Turkey) highway was investigated. Due to the landslide movement, problems have occurred in the existing road pavement, which has caused both safety problems and reduced driving comfort in the operation of the road. In order to model the landslide, drilling, geophysical and inclinometer studies were carried out in the field within the scope of ground investigation. Laboratory tests were carried out on soil and rock samples obtained from the borings. When the drilling and geophysical studies were evaluated together, it was determined that the study area has a complex geological structure. In addition, according to the inclinometer results, the direction and speed of movement of the landslide mass were observed. In order to create an idealized geological profile, all field and laboratory studies were evaluated together and then the sliding surface of the landslide was determined by back analysis method. According to the findings obtained, it was determined that the landslide was massively large, and the movement occurred had a deep sliding surface. As a result of the numerical analyses, it was concluded that the Slope angle reduction is the most economical and environmentally friendly method for the control of the landslide mass.

Keywords: landslide, geotechnical methods, geophysics, monitoring, highway

Procedia PDF Downloads 70

Authors: Sanskriti Singh, Ankit Tomar

Abstract:

Banks are the instrument of growth of a country. Banks mobilize the savings of the public in the form of deposits and channelize it as advances for various activities required for the development of society at large. The advance which becomes unpaid for a certain period is called Non Performing Asset of the bank. The study makes an attempt to bring out the magnitude of NPA and its impact on profit, advances. An attempt is also made to bring out the challenges NPA poses to the banks and suggestions to overcome and to manage NPA effectively.

Keywords: India, NPAs, private banks, public banks

Procedia PDF Downloads 287

Authors: S. Pal, R. Singh, S. Sivakumar, D. Kunzru

Abstract:

AOT stabilized reverse micelles of deionized water, dispersed in isooctane have been used to synthesize bimetallic nickel tungsten nanoparticles. Prepared nanoparticles were supported on γ-Al2O3 followed by calcination at 500oC. Characterizations of the nanoparticles were done by TEM, XRD, FTIR, XRF, TGA and BET. XRF results showed that this method gave good composition control with W/Ni weight ratio equal to 3.2. TEM images showed particle size of 5-10 nm. Removal of surfactant after calcination was confirmed by TGA and FTIR.

Keywords: nanoparticles, reverse micelles, nickel, tungsten

Procedia PDF Downloads 593

Authors: Hamidreza Torabi, Mohsen Moghtaderi

Abstract:

Introduction: Currently, deep vitrectomy with silicone oil tamponade is the standard treatment method for patients with Rhegmatogenous Retinal Detachment (RRD). After retinal repair, it is necessary to remove silicone oil from the eye, but the appropriate time to remove the oil and complications related to that time has been less studied. The aim of this study was to compare the results of the early removal of silicone oil with the delayed removal of silicone oil in patients with RRD. Method & material: Patients who were referred to the Ophthalmology Clinic of Baqiyatallah Hospital, Tehran, Iran, due to RRD with detached macula in 2021 & 2022 were evaluated. These patients were treated with deep vitrectomy and silicone oil tamponade. Patients whose retinas were attached after the passage of time were candidates for silicone oil removal (SOR) surgery. For patients in the early SOR group, SOR surgery was performed 3-6 months after the initial vitrectomy surgery, and for the late SOR group, SOR was performed after 6 months after the initial vitrectomy surgery. Results: In this study, 60 patients with RRD were evaluated. 23 (38.3%) patients were in the early group, and 37 (61.7%) patients were in the late group. Based on our findings, it was seen that the mean visual acuity of patients based on the Snellen chart in the early group (0.48 ± 0.23 Decimal) was better than the late group (0.33 ± 0.18 Decimal) (P-value=0.009). Retinal re-detachment has happened only in one patient with early SOR. Conclusion: Early removal of silicone oil (less than 6 months) from the eyes of patients undergoing RRD surgery has been associated with better vision results compared to late removal.

Keywords: retinal detachment, vitrectomy, silicone oil, silicone oil removal, visual acuity

Procedia PDF Downloads 79

Authors: Jean Marriz M. Manzano, Marc D. Rosales, Magdaleno R. Vasquez Jr., Maria Theresa G. De Leon

Abstract:

This paper discusses the effects of surface roughness on a cantilever beam vibrational energy harvester. A silicon sample was fabricated using MEMS fabrication processes. When etching silicon using deep reactive ion etching (DRIE) at large etch depths, rougher surfaces are observed as a result of increased response in process pressure, amount of coil power and increased helium backside cooling readings. To account for the effects of surface roughness on the characteristics of the cantilever beam, finite element method (FEM) modeling was performed using actual roughness data from fabricated samples. It was found that when etching about 550um of silicon, root mean square roughness parameter, Sq, varies by 1 to 3 um (at 100um thick) across a 6-inch wafer. Given this Sq variation, FEM simulations predict an 8 to148 Hz shift in the resonant frequency while having no significant effect on the output power. The significant shift in the resonant frequency implies that careful consideration of surface roughness from fabrication processes must be done when designing energy harvesters.

Keywords: deep reactive ion etching, finite element method, microelectromechanical systems, multiphysics analysis, surface roughness, vibrational energy harvester

Procedia PDF Downloads 121

Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

Abstract:

The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

Procedia PDF Downloads 13