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Prediction of foot risk classification for Type II Diabetic through image analysis 6

Prediction of foot risk classification for Type II Diabetic ...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Sheikh, Mehewish Musheer Balachandra, Mamatha Narendra, V.G. Maiya, Arun G Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Sceince & Engg Manipal India Manipal College of Health Professions Manipal Academy of Higher Education Dept. of Physiotherapy Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

The global prevalence of diabetes mellitus has increased. The use of electronic platform devices has grown in popularity due to their low cost and ease of use. Despite their benefits, however, there remain concerns about their accuracy and precision. The objective of the study is to determine the accuracy and precision of the Win-track platform. In a cross-sectional study, 49 male patients' data were collected. Based on the pressure asserted, the data were further classified into different stages from 1 to 4. The study used four different types of classifiers (Logistic, Multi-layer Perceptron, Simple Logistic Regression and Meta-logit Boost) to check the accuracy. The result shown for all the classifiers was positive with Meta-logit Boost giving the higher Mathews correlation coefficient (MCC) (stage 1=1, stage 2=1, stage 3=0.904 and stage 4=0.912) and highest correctly classified instances and lowest incorrectly classified instances (95.91% and 4.08% respectively) with least amount of time taken for execution (T = 0.02ms). With respect to the accuracy obtained, it is suggested to use the Win-track platform in hospitals and clinics. © 2022 IEEE.

关键词： Image classification

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Offline Mathematical Expression Evaluator using Non-Linear SVM

Offline Mathematical Expression Evaluator using Non-Linear S...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Susmitha Valli Gogula Venkata Dheeraj Reddy Pittala Ashrith Chirala Krishnam Raju Mantena Ashwin Raj Pillai Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, papers and transcripts. We perform Image Pre-processing steps like Image Segmentation, Character Segmentation, Normalization and Binarization to covert the Image data to appropriate training data for the model using OpenCV Library. The character images in the [OHME] are classified and recognized using Non-Linear Support Vector Machines [SVM] and they are converted to appropriate Strings. These Strings are solved using user-defined mathematical methods. These modules are used to evaluate these String expressions and the appropriate results are generated and plotted.

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Optimizing Cross-Chain DeFi and Smart Contracts in Stochastic Integration

Optimizing Cross-Chain DeFi and Smart Contracts in Stochasti...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Jongho Seol Jongyeop Kim Abhilash Kancharla dept. of information technology Middle Georgia State University Macon GA USA dept. of information technology Georgia Southern University Statesboro GA USA dept. of computer science The University of Tampa Tampa FL USA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

This research presents a sophisticated technological framework for Cross-Chain Decentralized Finance (DeFi) and Smart Contract systems by seamlessly integrating Markov Models, Brownian Motion, and Stationary Processes. Focused on enhancing the adaptability and efficiency of financial interactions across interconnected blockchain networks, this framework establishes the foundational elements necessary for dynamic system modeling. The incorporation of Markov Models captures state transitions, Brownian Motion models random fluctuations, and Stationary Processes ensure statistical stability. The paper explores the technological implications of these stochastic processes, addressing challenges in system interoperability, latency, and security within decentralized financial ecosystems. Envisioning a future where decentralized systems are optimized and resilient, the research investigates advancements in blockchain protocol design, consensus mechanisms, and transaction validation strategies. The proposed framework, influenced by the dynamic and statistical nature of Brownian Motion and Stationary Processes, underscores the need for robust data structures, real-time data feeds, and decentralized oracle networks. This research invites collaboration from the blockchain, smart contract, and stochastic modeling communities to contribute to the ongoing exploration and refinement of this powerful technological framework, poised to reshape the landscape of cross-chain financial technologies.

关键词： Brownian motion Adaptation models Biological system modeling Smart contracts Ecosystems Finance Stochastic processes

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Brain Tumor Detection with Ensemble of Convolutional Neural Networks and Vision Transformer

Brain Tumor Detection with Ensemble of Convolutional Neural ...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Mustafa Yurdakul Şakir TAŞDEMİR Computer Engineering Dept. Engineering Faculty Kırıkkale University Kırıkkale Turkey Computer Engineering Dept. Technology Faculty Selçuk University Konya Turkey

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our proposed model provides a satisfactory performance comparedto existing methods.

关键词： Magnetic resonance imaging Anatomical structure Brain modeling Transformers Convolutional neural networks Task analysis Artificial intelligence

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Intelligent System for Detection of Intrusion in Imbalanced Network Traffic

Intelligent System for Detection of Intrusion in Imbalanced ...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Piyush Bagla Amit Kumar Mishra Neeraj Kumar Pandey Ravi Sharma Ankur Dumka Department of Computer Science & Engineering National Institute of Technology Jalandhar India Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun (India) Department of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Science & Engineering Women Institute of Technology Dehradun Uttarakhand (India)

Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms.

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Resource Allocation for OTFS-Based ISAC Systems

Resource Allocation for OTFS-Based ISAC Systems

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Meiying Zhang Hengtao He Shenghui Song Jun Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Integrated sensing and communications (ISAC) has been considered as a key technology for future wireless networks. Orthogonal time frequency space (OTFS) modulation is considered as an effective technology to achieve ISAC in high-mobility scenarios due to its ability to combat high delay and Doppler shifts. However, although the performance analysis of OTFS-based ISAC system has been investigated, resource allocation and joint optimization for communication and sensing are still in its fancy. In this paper, we consider resource allocation in OTFS-based ISAC systems. We first introduce the system model and formulate the resource allocation problems. Then, several related works are discussed in detail. Next, an OTFS-based multiple-user multiple-input multiple-output (MIMO) ISAC system model is investigated, where the base station is considered as an ISAC transmitter and sends the message to different vehicles while simultaneously estimating their relative range and velocity via output feedback, respectively. We then formulate the sensing and communication trade-off optimization problem considering the delay Doppler resource block (DDRB) allocation and provide some sample results. Finally, we show several future directions for resource allocation in OTFS-based ISAC systems.

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A Dual-Band Multilayer SIW Cavity Backed Differential Slot Antenna

A Dual-Band Multilayer SIW Cavity Backed Differential Slot A...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Nayak, Anil Kumar Filanovsky, Igor M Moez, Kambiz Patnaik, Amalendu University of Alberta ICAS Laboratory Dept. of Electrical and Computer Engg. Alberta Canada Indian Institute of Technology Roorkee Advanced Microwave Laboratory Dept. of Electronics and Communication Engg. Roorkee India

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

This paper presents the design of a differential dual-band three-layer substrate integrated waveguide (SIW) cavity-backed antenna. The SIW has three cross-shaped slots and two shorting pins to facilitate radiation and improve impedance matching. The measured fractional bandwidth of 1.2 and 2.06%, peak gain of 5.67 and 6.99 dBi, radiation efficiency of 79.75 and 92.78%, and cross polarization level more than 37.94 dB have been achieved at resonating frequencies of 10.66 and 11.64 GHz, respectively. The proposed antenna is suitable for X-band application by comparing its output parameters with the previously reported results © 2022 IEEE.

关键词： Substrate integrated waveguides

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Harmonizing Individual and Group Interests: A Strategy for Fostering Cooperation in Social Dilemma Games 4

Harmonizing Individual and Group Interests: A Strategy for F...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Greenwood, Garrison W. Abbass, Hussein Hussein, Aya Portland State University Dept. of Elect. & Computer Engr. PortlandOR97207-0751 United States School of University of New South Wales CanberraACT2600 Australia University of Canberra Faculty of Science and Technology CanberraACT2617 Australia

ISBN: (纸本)9798350387063

Why humans cooperate remains an open question. Researchers use social dilemma games to try and find some answers. Unfortunately, over the last 3 decades arguably only slow progress has been made. This is due to a reliance on just fitness to evolve strategies which removes any player decision process. In this paper, we propose a new approach to evolving strategies in social dilemma games. This approach combines individual interests with group interests and injects a formal decision process into strategy selection. It combines the ideas of Adam Smith, the father of modern economics, and the work of Nobel laureate Lloyd Shapley. Our results from public goods game experiments show our approach can produce dramatically different strategy evolution but one which matches player preferences. This new approach provides a better framework for investigating cooperation in human populations. © 2024 IEEE.

关键词： Shapley values social dilemma games strategy evolution

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LDPSA: A Large Dataset of Persian Sentiment Analysis

LDPSA: A Large Dataset of Persian Sentiment Analysis

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International Conference on Web Research (ICWR)

作者： Ali Nazarizadeh Taha Samavati Sina Mansouri Minoo Sayyadpour Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Faculty of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran

ISBN: (数字)9798350394986

ISBN: (纸本)9798350394993

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-dept. analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper.

关键词： COVID-19 Sentiment analysis Electronic publishing Gold Data analysis Pandemics Reliability

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A Multi-Slice Encoding Direction Extraction Network for Palmprint Recognition 24

A Multi-Slice Encoding Direction Extraction Network for Palm...

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Proceedings of the 2024 3rd International Symposium on Computing and Artificial Intelligence

作者： Hao Yang Shuyi Li Bob Zhang PAMI Research Group Dept. of Computer and Information Science University of Macau Taipa Macao Beijing University of Technology Beijing China

ISBN: (纸本)9798400710674

Palmprint recognition stands out for its exceptional accuracy and accessibility, substantially enhancing personal security in mobile payments and identity verification applications, as well as efficiently distinguishing individuals through the unique patterns of a person's hand. The distribution of viable palmprint textures across images is frequently uneven, but follows a recognizable pattern. Typically, the upper region of the palm shows a dense arrangement of lines that are predominantly horizontal, whereas the lower region is characterized by a sparser distribution of lines that are more vertically aligned. However, existing palmprint recognition methods have not yet thoroughly explored the differences in textural directionality among various local regions of palmprints. To address this issue, we propose an innovative Multi-Slice Encoded Direction Extraction Network (MSEDNet) that segments the palmprint images into multiple smaller slices for palmprint recognition. Specifically, we design a Learnable Direction Extraction Block (LDEB) to adjust the sensitivity between the X and Y directions independently for each slice. The texture density within a slice is closely related to the relative positions between slices and the central area usually displays a denser texture than the peripheral regions. Furthermore, we develop a dual-branch structure that incorporates both the Relative Position Encoding Branch (RPEB) and the Texture Density Encoding Branch (TDEB) integrating the relative positional information between slices with the texture density information within each slice. Extensive experimental results on five public palmprint datasets demonstrated that our proposed method achieves remarkable recognition results.

关键词： Palmprint recognition

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