Search results for: Bal Deep Sharma

Authors: S. Alansary, M. Nagi

Abstract:

This paper presents an approach of conducting semantic annotation of Arabic corpus using the Universal Networking Language (UNL) framework. UNL is intended to be a promising strategy for providing a large collection of semantically annotated texts with formal, deep semantics rather than shallow. The result would constitute a semantic resource (semantic graphs) that is editable and that integrates various phenomena, including predicate-argument structure, scope, tense, thematic roles and rhetorical relations, into a single semantic formalism for knowledge representation. The paper will also present the Interactive Analysis​ tool for automatic semantic annotation (IAN). In addition, the cornerstone of the proposed methodology which are the disambiguation and transformation rules, will be presented. Semantic annotation using UNL has been applied to a corpus of 20,000 Arabic sentences representing the most frequent structures in the Arabic Wikipedia. The representation, at different linguistic levels was illustrated starting from the morphological level passing through the syntactic level till the semantic representation is reached. The output has been evaluated using the F-measure. It is 90% accurate. This demonstrates how powerful the formal environment is, as it enables intelligent text processing and search.

Keywords: semantic analysis, semantic annotation, Arabic, universal networking language

Procedia PDF Downloads 582

Authors: Mehrnaz Mostafavi, Mahtab Shabani, Alireza Azani, Fatemeh Ghafari

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Artificial intelligence (AI) can transform breast cancer diagnosis and therapy by providing sophisticated solutions for screening, imaging interpretation, histopathological analysis, and treatment planning. This literature review digs into the many uses of AI in breast cancer treatment, highlighting the need for collaboration between AI scientists and healthcare practitioners. It emphasizes advances in AI-driven breast imaging interpretation, such as computer-aided detection and diagnosis (CADe/CADx) systems and deep learning algorithms. These have shown significant potential for improving diagnostic accuracy and lowering radiologists' workloads. Furthermore, AI approaches such as deep learning have been used in histopathological research to accurately predict hormone receptor status and categorize tumor-associated stroma from regular H&E stains. These AI-powered approaches simplify diagnostic procedures while providing insights into tumor biology and prognosis. As AI becomes more embedded in breast cancer care, it is crucial to ensure its ethical, efficient, and patient-focused implementation to improve outcomes for breast cancer patients ultimately.

Keywords: breast cancer, artificial intelligence, cancer diagnosis, clinical practice

Procedia PDF Downloads 72

Authors: Shwadhin Sharma, Monica Perez, Vinita Patel, Tyler Kuwatani, Siobhan Scott

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The purpose of this research is to explain and analyze why the Internet of Things (IoT) is an emerging technology trend. The aspects of this research paper include an overview of IoT, what research has already been done, the benefits, implications, and our own perspectives on the trend in order to thoroughly analyze how the trend of IoT will make an impact on society. Through the identification of what makes IoT important, it is concluded that IoT will have a tremendous impact for the whole world. Technology is never going to go away, it is going to get smarter and have the potential to change the world.

Keywords: internet of things, enablers of IoT, cost of IoT, benefits of IoT

Procedia PDF Downloads 358

Authors: Anurag Sharma, Manoj Kumar, Ashima, Sooraj Parkash

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The work in this paper presents simulative comparison for different modulation formats such as NRZ, Manchester and CRZ in a 100 subscribers at 5 Gbps bit rate Gigabit Ethernet Passive Optical Network (GEPON) FTTH network. It is observed from the simulation results that the CRZ modulation format is best suited for the designed system. A link design for 1:100 splitter is used as Passive Optical Network (PON) element which creates communication between central offices to different users. The Bit Error Rate (BER) is found to be 2.8535e-10 at 5 Gbit/s systems for CRZ modulation format.

Keywords: PON , FTTH, OLT, ONU, CO, GEPON

Procedia PDF Downloads 709

Authors: Rahul Mohite, Lahcen Ouarbya

Abstract:

This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

Procedia PDF Downloads 123

Authors: Sefik Can Karakaya, Ibrahim Demir

Abstract:

In this study, the relationship between musical preferences and personality traits has been investigated in terms of Spotify audio analysis features. The aim of this paper is to build such a classifier capable of segmenting people into their Myers-Briggs Type Index (MBTI) personality type based on their Spotify playlists. Music takes an important place in the lives of people all over the world and online music streaming platforms make it easier to reach musical contents. In this context, the motivation to build such a classifier is allowing people to gain access to their MBTI personality type and perhaps for more reliably and more quickly. For this purpose, logistic regression and deep neural networks have been selected for classifier and their performances are compared. In conclusion, it has been found that musical preferences differ statistically between personality traits, and evaluated models are able to distinguish personality types based on given musical data structure with over %60 accuracy rate.

Keywords: myers-briggs type indicator, music psychology, Spotify, behavioural user profiling, deep neural networks, logistic regression

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Authors: Anastassis Kozanitis, Diane Leduc, Alain Stockless

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This paper presents preliminary results from a two-year funded research program looking to analyze and understand the relationship between high cognitive engagement, higher order cognitive processes employed in situations of complex learning tasks, and the use of active learning pedagogies in engineering undergraduate programs. A mixed method approach was used to gauge student engagement and their cognitive processes when accomplishing complex tasks. Quantitative data collected from the self-report cognitive engagement scale shows that deep learning approach is positively correlated with high levels of complex learning tasks and the level of student engagement, in the context of classroom active learning pedagogies. Qualitative analyses of in depth face-to-face interviews reveal insights into the mechanisms influencing students’ cognitive processes when confronted with open-ended problem resolution. Findings also support evidence that students will adjust their level of cognitive engagement according to the specific didactic environment.

Keywords: cognitive engagement, deep and shallow strategies, engineering programs, higher order cognitive processes

Procedia PDF Downloads 324

Authors: Siddarth Kannan

Abstract:

Objectives: Deep Brain Stimulation (DBS) and Motor Cortex stimulation (MCS) are innovative interventions in order to treat various neuropathic pain disorders such as post-stroke pain. While each treatment has a varying degree of success in managing pain, comparative analysis has not yet been performed, and the success rates of these techniques using validated, objective pain scores have not been synthesised. The aim of this study was to compare the effect of pain relief offered by MCS and DBS on patients with post-stroke pain and to assess if either of these procedures offered better results. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines (PROSPEROID CRD42021277542). Three databases were searched, and articles published from 2000 to June 2023 were included (last search date 25 June 2023). Meta-analysis was performed using random effects models. We evaluated the performance of DBS or MCS by assessing studies that reported pain relief using the Visual Analogue Scale (VAS). Data analysis of descriptive statistics was performed using SPSS (Version 27; IBM; Armonk; NY; USA). R statistics (Rstudio Version 4.0.1) was used to perform meta-analysis. Results: Of the 478 articles identified, 27 were included in the analysis (232 patients- 117 DBS & 115 MCS). The pooled number of patients who improved after DBS was 0.68 (95% CI, 0.57-0.77, I2=36%). The pooled number of patients who improved after MCS was 0.72 (95% CI, 0.62-0.80, I2=59%). Further sensitivity analysis was done to include only studies with a minimum of 5 patients in order to assess if there was any impact on the overall results. Nine studies each for DBS and MCS met these criteria. There seemed to be no significant difference in results. Conclusions: The use of surgical interventions such as DBS and MCS is an upcoming field for the treatment of post-stroke pain, with limited studies exploring and comparing these two techniques. While our study shows that MCS might be a slightly better treatment option, further research would need to be done in order to determine the appropriate surgical intervention for post-stroke pain.

Keywords: post-stroke pain, deep brain stimulation, motor cortex stimulation, pain relief

Procedia PDF Downloads 139

Authors: Edemealem Desalegn Kingawa, Kafte Tasew Timkete, Mekashaw Girmaw Abebe, Terefe Feyisa, Abiyot Bitew Mihretie, Senait Teklemarkos Haile

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Currently, hate speech is a growing challenge for society, individuals, policymakers, and researchers, as social media platforms make it easy to anonymously create and grow online friends and followers and provide an online forum for debate about specific issues of community life, culture, politics, and others. Despite this, research on identifying and detecting hate speech is not satisfactory performance, and this is why future research on this issue is constantly called for. This paper provides a systematic review of the literature in this field, with a focus on approaches like word embedding techniques, machine learning, deep learning technologies, hate speech terminology, and other state-of-the-art technologies with challenges. In this paper, we have made a systematic review of the last six years of literature from Research Gate and Google Scholar. Furthermore, limitations, along with algorithm selection and use challenges, data collection, and cleaning challenges, and future research directions, are discussed in detail.

Keywords: Amharic hate speech, deep learning approach, hate speech detection review, Afaan Oromo hate speech detection

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Authors: A. Soni, D. R. Mishra, D. K. Koul

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α-Al2O3:C has been reported to have deeper traps at 600°C and 900°C respectively. These traps have been reported to accessed at relatively earlier temperatures (122 and 322 °C respectively) using thermally assisted OSL (TA-OSL). In this work, the dose response α-Al2O3:C was studied in the dose range of 10Gy to 10kGy for its application in food irradiation in low ( upto 1kGy) and medium(1 to 10kGy) dose range. The TOL (Thermo-optically stimulated luminescence) measurements were carried out on RisØ TL/OSL, TL-DA-15 system having a blue light-emitting diodes (λ=470 ±30nm) stimulation source with power level set at the 90% of the maximum stimulation intensity for the blue LEDs (40 mW/cm2). The observations were carried on commercial α-Al2O3:C phosphor. The TOL experiments were carried out with number of active channel (300) and inactive channel (1). Using these settings, the sample is subjected to linear thermal heating and constant optical stimulation. The detection filter used in all observations was a Hoya U-340 (Ip ~ 340 nm, FWHM ~ 80 nm). Irradiation of the samples was carried out using a 90Sr/90Y β-source housed in the system. A heating rate of 2 °C/s was preferred in TL measurements so as to reduce the temperature lag between the heater plate and the samples. To study the dose response of deep traps of α-Al2O3:C, samples were irradiated with various dose ranging from 10 Gy to 10 kGy. For each set of dose, three samples were irradiated. In order to record the TA-OSL, initially TL was recorded up to a temperature of 400°C, to deplete the signal due to 185°C main dosimetry TL peak in α-Al2O3:C, which is also associated with the basic OSL traps. After taking TL readout, the sample was subsequently subjected to TOL measurement. As a result, two well-defined TA-OSL peaks at 121°C and at 232°C occur in time as well as temperature domain which are different from the main dosimetric TL peak which occurs at ~ 185°C. The linearity of the integrated TOL signal has been measured as a function of absorbed dose and found to be linear upto 10kGy. Thus, it can be used for low and intermediate dose range of for its application in food irradiation. The deep energy level defects of α-Al2O3:C phosphor can be accessed using TOL section of RisØ reader system.

Keywords: α-Al2O3:C, deep traps, food irradiation, TA-OSL

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Authors: Rahul Malhotra, Anurag Sharma

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: FTTH, quad play, play service, access networks, data rate

Procedia PDF Downloads 417

Authors: Mohita Gangwar Sharma

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Different factors are compelling manufacturers to move towards the phenomenon of servitization i.e. when firms go beyond giving support to the customers in operating the equipment. The challenges that are being faced in this transition by the manufacturing firms from being a product provider to a product- service provider are multipronged. Product-Service Systems (PSS) lies in between the pure-product and pure-service continuum. Through this study, we wish to understand the dimensions of ‘PSS-quality’. We draw upon the quality literature for both the product and services and through an expert survey for a specific transportation sector using analytical hierarchical process (AHP) derive a conceptual model that can be used as a comprehensive measurement tool for PSS offerings.

Keywords: servitisation, quality, product-service system, AHP

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Authors: Monty Vacura

Abstract:

Hamilton’s Rule is a universal law of biology expressed in protists, plants and animals. When applied to human populations, this model explains: 1) Origin of religion in society as a biopsychological need naturally selected to increase population size; 2) Instincts of racism expressed through intergroup competition; 3) Simultaneous selection for human cooperation and conflict, love and hate; 4) Places Dawkins’s selfish gene as the r, relationship variable; 5) Flipping the equation variable themes (close relationship to distant relationship, and benefit to threat) the new equation can now be used to identify the offensive and defensive sides of conflict; 6) Connection between sporting events and instinctive social messaging for stimulating offensive and defensive responses; 6) Pathway to reduce human sacrifice through manipulation of variables. This paper discusses the deep psychological influences of Hamilton’s Rule. Suggestions are provided to reduce human deaths via our instinctive sacrificial behavior, by consciously monitoring Hamilton’s Rule variables highlighted throughout our media outlets.

Keywords: psychology, Hamilton’s rule, evolution, human instincts

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Authors: Gao Youtao, Zhao Tanran, Jin Bingyu, Xu Bo

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Global navigation satellite system(GNSS) can deliver navigation information for spacecraft orbiting on low-Earth orbits and medium Earth orbits. However, the GNSS cannot navigate the spacecraft on high-Earth orbit or deep space probes effectively. With the deep space exploration becoming a hot spot of aerospace, the demand for a deep space satellite navigation system is becoming increasingly prominent. Many researchers discussed the feasibility and performance of a satellite navigation system on periodic orbits around the Earth-Moon libration points which can be called Lagrangian point satellite navigation system. Autonomous orbit determination (AOD) is an important performance for the Lagrangian point satellite navigation system. With this ability, the Lagrangian point satellite navigation system can reduce the dependency on ground stations. AOD also can greatly reduce total system cost and assure mission continuity. As the elliptical restricted three-body problem can describe the Earth-Moon system more accurately than the circular restricted three-body problem, we study the autonomous orbit determination of Lagrangian navigation constellation using only crosslink range based on elliptical restricted three body problem. Extended Kalman filter is used in the autonomous orbit determination. In order to compare the autonomous orbit determination results based on elliptical restricted three-body problem to the results of autonomous orbit determination based on circular restricted three-body problem, we give the autonomous orbit determination position errors of a navigation constellation include four satellites based on the circular restricted three-body problem. The simulation result shows that the Lagrangian navigation constellation can achieve long-term precise autonomous orbit determination using only crosslink range. In addition, the type of the libration point orbit will influence the autonomous orbit determination accuracy.

Keywords: extended Kalman filter, autonomous orbit determination, quasi-periodic orbit, navigation constellation

Procedia PDF Downloads 283

Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

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Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

Procedia PDF Downloads 141

Authors: Romisaa Ali

Abstract:

This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

Procedia PDF Downloads 91

Authors: Shubham Bhonde, Nekzad Doctor, Shashwat Gawande

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For the use and the import of Keys and ID Transmitter as well as Body Control Modules with radio transmission in a lot of countries, homologation is required. Final deliverables in homologation of the product are certificates. In considering the world of homologation, there are approximately 200 certificates per product, with most of the certificates in local languages. It is challenging to manually investigate each certificate and extract relevant data from the certificate, such as expiry date, approval date, etc. It is most important to get accurate data from the certificate as inaccuracy may lead to missing re-homologation of certificates that will result in an incompliance situation. There is a scope of automation in reading the certificate data in the field of homologation. We are using deep learning as a tool for automation. We have first trained a model using machine learning by providing all country's basic data. We have trained this model only once. We trained the model by feeding pdf and jpg files using the ETL process. Eventually, that trained model will give more accurate results later. As an outcome, we will get the expiry date and approval date of the certificate with a single click. This will eventually help to implement automation features on a broader level in the database where certificates are stored. This automation will help to minimize human error to almost negligible.

Keywords: homologation, re-homologation, data science, deep learning, machine learning, ETL (extract transform loading)

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Authors: Omer Cahana, Maya Herman, Ofer Levi

Abstract:

Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

Procedia PDF Downloads 163

Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

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This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

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Authors: Faouzi Ahtchi-Ali, Marcus Vitiello

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A study was conducted in the east region of the Middle East to assess the constructability of a shoring system for a 12-meter deep excavation. Several shoring systems were considered in this study including secant concrete piling, contiguous concrete piling, and sheet-piling. The excavation was carried out in a very dense sand with the groundwater level located at 3 meters below ground surface. The study included conducting a pilot test for each shoring system listed above. The secant concrete piling included overlapping concrete piles to a depth of 16 meters. Drilling method with full steel casing was utilized to install the concrete piles. The verticality of the piles was a concern for the overlap. The contiguous concrete piling required the installation of micro-piles to seal the gap between the concrete piles. This method revealed that the gap between the piles was not fully sealed as observed by the groundwater penetration to the excavation. The sheet-piling method required pre-drilling due to the high blow count of the penetrated layer of saturated sand. This study concluded that the sheet-piling method with pre-drilling was the most cost effective and recommended a method for the shoring system.

Keywords: excavation, shoring system, middle east, Drilling method

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Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Chhaya Sharma, Dushyant Kumar

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The present work deals with the treatment of wastewater generated by paper industry by using aluminium as anode material. The quantitative and qualitative analyses of chloropenolics have been carried out by using primary clarifier effluent with the help of gas chromatography mass spectrometry. Sixteen chlorophenolics compounds have been identified and estimated. Results indicated that among 16 identified compounds, 7 are 100% removed and overall 66% reduction in chorophenolics compounds have been detected. Moreover, during the treatment, the biodegradability index of wastewater significantly increases, along with 70 % reduction in chemical oxygen demand and 99 % in color.

Keywords: aluminium anode, chlorophenolics, electrocoagulation, pollution load, wastewater

Procedia PDF Downloads 348

Authors: David Pham, Yongfeng Zhang

Abstract:

Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

Procedia PDF Downloads 79

Authors: Min Jin Kwon, Seung Won Kim, Eui Yeon Kim, Haeyoung Lee

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Numerous anonymous chat apps that help people to connect with random strangers have been released in South Korea. However, they become a serious problem for young people since young people often use them for channels of prostitution or sexual violence. Although ISPs in South Korea are responsible for making inappropriate content inaccessible on their networks, they do not block traffic of random chat apps since 1) the use of random chat apps is entirely legal. 2) it is reported that they use HTTP proxy blocking so that non-HTTP traffic cannot be blocked. In this paper, we propose a service model that can block random chat apps at home routers. A service provider manages a blacklist that contains blocked apps’ information. Home routers that subscribe the service filter the traffic of the apps out using deep packet inspection. We have implemented a prototype of the proposed model, including a centralized server providing the blacklist, a Raspberry Pi-based home router that can filter traffic of the apps out, and an Android app used by the router’s administrator to locally customize the blacklist.

Keywords: deep packet inspection, internet filtering, juvenile protection, technical blocking

Procedia PDF Downloads 350

Authors: Shubham Gogri, Lucia Florescu

Abstract:

Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

Procedia PDF Downloads 164

Authors: E. E. Tereshatov, M. Yu. Boltoeva, V. Mazan, M. F. Volia, C. M. Folden III

Abstract:

Room temperature ionic liquids (RTILs) are a class of liquid organic salts with melting points below 20 °C that are considered to be environmentally friendly ‘designers’ solvents. Pure hydrophobic ILs are known to extract metallic species from aqueous solutions. The closest analogues of ionic liquids are deep eutectic solvents (DESs), which are a eutectic mixture of at least two compounds with a melting point lower than that of each individual component. DESs are acknowledged to be attractive for organic synthesis and metal processing. Thus, these non-volatile and less toxic compounds are of interest for critical metal extraction. The US Department of Energy and the European Commission consider indium as a key metal. Its chemical homologue, thallium, is also an important material for some applications and environmental safety. The aim of this work is to systematically investigate In and Tl extraction from aqueous solutions into pure fluorinated ILs and hydrophobic DESs. The dependence of the Tl extraction efficiency on the structure and composition of the ionic liquid ions, metal oxidation state, and initial metal and aqueous acid concentrations have been studied. The extraction efficiency of the TlXz3–z anionic species (where X = Cl– and/or Br–) is greater for ionic liquids with more hydrophobic cations. Unexpectedly high distribution ratios (> 103) of Tl(III) were determined even by applying a pure ionic liquid as receiving phase. An improved mathematical model based on ion exchange and ion pair formation mechanisms has been developed to describe the co-extraction of two different anionic species, and the relative contributions of each mechanism have been determined. The first evidence of indium extraction into new quaternary ammonium- and menthol-based hydrophobic DESs from hydrochloric and oxalic acid solutions with distribution ratios up to 103 will be provided. Data obtained allow us to interpret the mechanism of thallium and indium extraction into ILs and DESs media. The understanding of Tl and In chemical behavior in these new media is imperative for the further improvement of separation and purification of these elements.

Keywords: deep eutectic solvents, indium, ionic liquids, thallium

Procedia PDF Downloads 242

Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 736