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Disruptive Technologies (ICDT), International Conference on

作者： P. Iyappan Shikha Maheshwari A. Saranya M. Jayaprakash School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Directorate of Online Education Manipal University Jaipur Rajasthan India Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of IT RMK Engineering College Kavaraipettai Gummidipoondi Tamil Nadu India

The Internet of Things (IoT) is an open network model that aims to build and link the interactions between the devices and links. Conventional blockchain model aimed at increase the scalability but often it is limited by its capacity and performance. The deep learning algorithms aims to determine the parameters of the blockchain that finds the optimal value required to obtain an increased scalability without any limitations in its performance. In this paper, a deep learning model is integrated with the blockchain to improve the process of communication in a secured way. The deep learning model optimizes the necessary security parameters required to transfer the data in a secured way. The experimental validation shows an increased scalable task allocation than its predecessors.

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Narrow Band Internet of Things (NB-IoT) for Wireless Communication and Network Optimization

Narrow Band Internet of Things (NB-IoT) for Wireless Communi...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Smita Khond Kanegonda Ravi Chythanya Shaik Munawar Manjusha Mandava Kruthika C G IT Department Malla Reddy Engineering College for Women Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Kakatiya Institute of Technology and Science Warangal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

This research presents an all-encompassing method for improving Narrow Band Internet of Things (NB-IoT) network performance by including network design, interference management, power control techniques, and security measures. Planning your network ahead of time is essential, and will involve decisions like network design, base station placement, and channel allocation. The second stage, interference control, seeks to reduce background noise emanating from various sources. The third and last stage is to use transmission power management algorithms for NB-IoT devices. In real-time, these algorithms optimize power levels for the channel and the task at hand. In the last stage, we apply security measures including authentication, encryption, and other types of network access control. According to the data, the suggested approach would have cost a total of $500,000, but more conventional approaches would have cost between $700,000 and $900,000. Also, the suggested method had an average delay of 1.5 seconds.

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CSO — VGG-19: Prediction of Cervical Cancer Using Cuckoo Search-Based Deep VGG-19

CSO — VGG-19: Prediction of Cervical Cancer Using Cuckoo Se...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： M.Suresh Anand S. Manimozhi Abul Ala Walid A Naresh Kumar P. Dharmendra Kumar N.V.S. Suryanarayana Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Department of Computer Science and Applications Periyar Maniammai Institute of Science and Technology Vallam Thanjavur Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Department of Computer Science and Business Systems Sri Sairam Engineering College West Tambaram Chennai Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India HVPE Central Tribal University of Andhra Pradesh Vizianagaram

Nearly four percent of women worldwide have cervical cancer, which can be fatal if not detected and treated early. When contrasted to current numbers, the death rate a few decades ago was unacceptably high. In recent years, clinics have intended to employ advancements in digital image and machine learning to improve cervical cancer screening. Women that test negative have a low risk of developing cancer of the cervical cavity over the following ten years, even if the majority of cervical infections that result in positive testing do not cause precancer. For the prompt identification as well as prompt cure for cervical cancer, it is crucial to identify important genes in cancer, identify healthy people as cervical cancer predictors, and perform an initial evaluation of a high-through expression information. The major objective of the suggested effort is to identify cervical cancer in order to forecast cervical genes. To find the cervical genes, a cuckoo-based deep conversion is created. To some degree, the proposed model predicts the progression of cervical cancer using the VGG-19 model. The deep conversion-based cuckoo search technique can be used to identify cervical cancer patients. The proposed model obtains a 99.30% accuracy rate.

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Twitter Spam Detection Using Word Vector and Binary Classifier

Twitter Spam Detection Using Word Vector and Binary Classifi...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Ramaprabha Marimuthu Gurusigaamani Ayyanar Muthulingam Vinoth N.A. S Sivasathiya Ganesan Kardeepa Ponnuchamy Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.

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Genetic Signal Processing for Categorizing Genomic Data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Padmaja Pulicherla Srinivas Aluvala Munimanda Premchander Poranki V L R N Sai Sudha Revanasiddappa Department of CSE Institute of Technology and Management Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95).

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Deep Neural Pretraining for Improving Diagnostic and Therapeutic Entity Recognition in Electronic Medical Records

Deep Neural Pretraining for Improving Diagnostic and Therape...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Shreya Patchala Enquero G Sunil Suvarna Sunil Kumar Chunduri Lavanya Prateeksha Siddhanti Global-A Genpact Company Bangalore India School of Computer Science & Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Hyderabad Dhulapally India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru Yelahanka India

The predominance of compound agency, a lack of utterance components, and unclear entity boundaries all contribute to difficulties in distinguishing diagnostic and treatment entities in EMRs. Furthermore, obtaining electronic medical records in China may be difficult. A deep learning strategy that uses deep neural pretraining to improve diagnostic and therapeutic entity recognition in EMRs is proposed. A BERT-CRF model example is shown using a dataset of 10,000 EMRs. This paradigm combines the benefits of pretraining with the adaptability of CRF. The outcomes of the study show that deep brain pretraining might be used to improve the performance of NLP applications in the healthcare business. The use of a domain-specific corpus learning technique and an entity recognition model trained on a training dataset improves word embeddings. The suggested system achieved an accuracy (0.973), precision (0.909), recall (0.896), f1-score (0.902) and MCC (0.893) respectively.

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Expression of Concern for: Investigating the Effect of Different Machine Learning Algorithms on Text Classification Outcomes

Expression of Concern for: Investigating the Effect of Diffe...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Mudasir Manzoor Kirmani Razauddin Bechoo Lal U. Sesadri Dharmesh Dhabliya Sudhir Kumar Chaturvedi Department of Division of Social Science SKUAST Kashmir Information and Computer Science Gulf Colleges Hafar Al Batin Saudia Arabia Department of Computer science and Engineering Konenru Lakshmaiah Education Foundation Vaddeswaram AP India Department of CSE Vardhaman College of Engineering Shamshabad Hyderabad Telangana Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India UPES Department of School of Engineering Dehradun India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This observe examined the impact of different system learning algorithms on textual content class results. Guide Vector Machines, Logistic Regression, Naïve Bayes, k nearest acquaintances and choice timber have been employed for this purpose. The dataset used includes critiques of merchandise made by way of real clients. A hard and fast of opinions changed into tagged with ‘fantastic’ or ‘bad’ labels. The aim of the observe become to analyse if the accuracy improves through changing the device gaining knowledge of algorithms used for classifying the opinions. Via using move validation and parameter tuning it turned into found that aid Vector Machines furnished the very best accuracy price of eighty four%, the lowest accuracy fee many of the ML algorithms carried out become located for Naïve Bayes at seventy nine%. It became concluded that ML algorithms which includes SVM ought to be preferentially be employed for comparable varieties of responsibilities.

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Enabling Mmwave Connectivity in 5G Base Stations: Key Requirements Overview

Enabling Mmwave Connectivity in 5G Base Stations: Key Requir...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Nikhil Garg Rahul Vishnoi Bharat Singh K. Ranjith Singh S. Kannimuthu Monali Ravindra Borade Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of ECE TMU School of COMPUTER SCIENCE AND APPLICATIONS IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Information Technology Karpagam College of Engineering Coimbatore AI & Ds Vishwakarma Institute of Information Technology Pune

The search for novel solutions to allow the era networks because the various difficulties be solved before connections in wireless networks can be fully characterized. Many essential components to facilitate communications are included in this study. The explanation of channel information on signal caused by air gases, rain, and open space propagation. It is detailed how the design works. The blocking effect in caused by penetration loss is given, along with potential solutions. It is suggested to use as well as to use it backhaul. Introduction Throughout the course of multiple generations of development, e seen enormous technological advancements and economic success. The term “s” describes the most fundamental analogue voice-only systems using brick phones s. phone use has skyrocketed as a result of revolutionary totally digital networks. offered, and voice quality had significantly improved. surfing are examples data applications that may be offered.

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POAR: Towards Open Vocabulary Pedestrian Attribute Recognition

arXiv

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arXiv 2023年

作者： Zhang, Yue Weng, Zhenyu Wang, Suchen Cen, Yigang Kan, Shichao Tan, Yap-Peng College of Computer and Information Engineering Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Normal University Xinxiang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Institute of Information Science Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing China School of Computer Science and Engineering Central South University Changsha China

Pedestrian attribute recognition (PAR) aims to predict the attributes of a target pedestrian. Recent methods often address the PAR problem by training a multi-label classifier with predefined attribute classes, but they can hardly exhaust all possible pedestrian attributes in the real world. To tackle this problem, we propose a novel Pedestrian Open-Attribute Recognition (POAR) approach by formulating the problem as a task of image-text search. Our approach employs a Transformer-based Encoder with a Masking Strategy (TEMS) to focus on the attributes of specific pedestrian parts (e.g., head, upper body, lower body, feet, etc.), and introduces a set of attribute tokens to encode the corresponding attributes into visual embeddings. Each attribute category is described as a natural language sentence and encoded by the text encoder. Then, we compute the similarity between the visual and text embeddings to find the best attribute descriptions for the input images. To handle multiple attributes of a single pedestrian, we propose a Many-To-Many Contrastive (MTMC) loss with masked tokens. In addition, we propose a Grouped Knowledge Distillation (GKD) method to minimize the disparity between visual embeddings and unseen attribute text embeddings. We evaluate our proposed method on three PAR datasets with an open-attribute setting. The results demonstrate the effectiveness of our method as a strong baseline for the POAR task. Our code is available at https://***/IvyYZ/POAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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