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International conference on Computer and Applications (ICCA)

作者： Md. Fahim Md. Ekhtiar Uddin Rizve Ahmed Md. Rashedul Islam Nadeem Ahmed Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (纸本)9781665452953

Climate change has huge impact in human health. Social and environmental determinants of health are affected by climate change. According to World Health Organization (WHO) states that the strokes, most heart diseases, cancers, diabetes, chronic kidney diseases are the top causes of death. Although there are direct factors behind these diseases, climate change could have an invisible role in the rise of these diseases. Researchers are using various technologies to find correlations between climate change and human health, particularly trying to find out which elements of the weather are more responsible. Although there are explicit reasons for the formation of these diseases. But few studies have been conducted on passive factors that have a hidden but serious effect on the formation of these diseases. In this regard, machine learning approach can help us to correlate between the features of climate and various human diseases. Following that, the study uses Pearson, Spearman and Phi-K algorithms to determine the possibilities of correlation between human health and climate change. The research states that Carbon Monoxide (CO) have 98% of correlation and carbon dioxide (CO 2 ) has 95% of correlation with cardiovascular disease (Ca).

关键词： Climate change Correlation machine learning algorithms Pulmonary diseases machine learning Organizations Nervous system

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OFDMA Based UAVs Communication for Ensuring QoS

OFDMA Based UAVs Communication for Ensuring QoS

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International conference on Advances and Applications of Artificial Intelligence and machine learning, ICAAAIML 2021

作者： Karabulut, Muhammet Ali Shahen Shah, A.F.M. Islam, Md Baharul Rana, Muhammad Ehsan Department of Electrical and Electronics Engineering Kafkas University Kars Turkey Department of Electronics and Communication Engineering Yildiz Technical University Istanbul Turkey School of Data Science and Engineering American University of Malta Bormla Malta School of Computing Asia Pacific University of Technology and Innovation Kuala Lumpur Malaysia

ISBN: (纸本)9789811948305

Unmanned aerial vehicles (UAV), more often familiar as drone, has been widely used both in civilian activities and military missions. UAV ad-hoc networks, also known as flying ad-hoc networks, are ad-hoc multi-UAV systems (FANETs). The FANET’s medium access control (MAC) and physical (PHY) layer specifications are defined by the IEEE 802.11 standard, which uses the enhanced distributed channel access function (EDCAF) to assure the quality of service (QoS) in the MAC layer. Because it suffers from collisions, carrier sensing multiple access with collision avoidance (CSMA/CA) is useless when a high data rate is required. As a result, the use of orthogonal frequency division multiple access (OFDMA) is advised. By lowering the collision probability, OFDMA increases performance and reduces latency. This study develops an analytical model for assessing IEEE 802.11 EDCAF performance for OFDMA-based UAV communication. A new channel contention method that combines EDCAF and OFDMA is suggested. Firstly, EDCA based prioritized channel contention is done. Then, channel access and transmission are done using OFDMA. We undertake a theoretical study based on the Markov chain model, which gives throughput, packet drop rate (PDR), and delay expressions. The simulation results verify the model performance and show that the proposed technique ensures strict delay requirements and supports QoS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency division multiple access

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Sentimental Analysis of Movie Review using machine learning Approach

Sentimental Analysis of Movie Review using Machine Learning ...

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Current Development in Engineering and Technology (CCET), IEEE International conference on

作者： Md.Thoufiq Zumma K.B.M Tahmiduzzaman Raihan Khan Nashed Shah Roni A.A.M Rahat-Bin-Rafique Abdul Hady Akash Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

A key area of machine learning called sentiment analysis seeks to extract subjective data from textual evaluations. The most popular technique for anticipating user ratings is sentiment analysis, and several machine-learning techniques have been employed to provide precise predictions. Sentiment analysis is the skill of examining information regarding what the general public really thinks about your company, a text, an opinion, a social media post, etc. It is a very potent tool in the analytics toolbox. Natural language processing and text mining both have a close connection to the study of sentiment. It can be used to evaluate the reviewer's viewpoint on certain topics or the review's overall polarity. The accuracy of the model is evaluated using sentiment analysis on the IMDB movie reviews dataset utilizing machine learning (ML) and natural language processing (NLP) techniques. Natural language processing and machine learning combine to provide the fundamental building blocks of sentiment analysis. Provides context to grasp the meaning of any text by enhancing the capabilities of machine learning and natural language processing. Using machine learning classification methods, this study suggests a prediction model for the sentiment analysis of movie reviews. This study aids researchers in choosing the most effective method for doing accurate and timely emotive analysis on IMDB movie reviews. Here, want to estimate the general polarity of the review using machine learning and natural language processing (NLP).

关键词： Training Text mining Sentiment analysis Analytical models machine learning algorithms Social networking (online) Motion pictures

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1st International conference on Cyber Warfare, Security and Space Research, SpacSec 2021

1st International Conference on Cyber Warfare, Security and ...

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1st International conference on Cyber Warfare, Security and Space Research, SpacSec 2021

ISBN: (纸本)9783031157837

The proceedings contain 25 papers. The special focus in this conference is on Cyber Warfare, Security and Space Research. The topics include: Privacy Preserving and Secured Clustering of Distributed Data Using Self Organizing Map;implementation of Intrusion Detection Systems in Distributed Architecture;Practical Phase Shift Model for RIS-Assisted D2D Communication Systems;recent Trend of Transform Domain Image Steganography Technique for Secret Sharing;design of a High Efficiency Access Control and Authentication Analysis Model for Cloud Deployments Using Pattern Analysis;A Study on Cyber Security in Various Critical IT Infrastructure Organizational Sectors: Challenges and Solutions;Short-Delay Multipath Errors in NavIC Signals for Stationary Receivers;point Clouds Object Classification Using Part-Based Capsule Network;study of Various Attacks Over Images Transferred Optically Through Communication Channel;Brain Tumor Classification via UNET Architecture of CNN Technique;Satellite Image Classification Using ANN;power Quality Disturbance Classification Using Transformer Network;on-Orbit, Non-destructive Surface Surveillance and Inspection with Convolution Neural Network;ramadan Group Transform Fundamental Properties and Some its Dualities;enhancing Security Mechanism in Smart Phones Using Crowdsourcing;INTELLIBOT - Intelligent Voice Assisted Chatbot with Sentiment Analysis, COVID Dashboard and Offensive Text Detection;analysis of Various Supervised machine learning algorithms for Intrusion Detection;enhanced Security Mechanism for Cryptographic File Systems Using Trusted computing;critical Analysis of IoT Ecosystem to Understand Security Threats and Challenges;hyper-parameters Study for Breast Cancer Datasets: Enhancing Image Security and Accuracy for Prediction Class;An Efficient Image Encryption Technique Using Logistic Map and 2D-TCLM;learning Co-occurrence Embeddings for Sentence Classification;Preventing Fault Attacks on S-Boxes of AES-Like Block Ciphers.

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Comparative Study of Deep learning models to Recognize Palm Leaf Malayalam Characters

Comparative Study of Deep Learning models to Recognize Palm ...

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International conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Remya Sivan Suja Palaniswamy Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9781665457002

Palm-leaf manuscripts are historical records written mainly in many local languages in many South asian countries. These texts have suffered damage over many years and centuries. Many organizations have scanned and digitally imaged palm-leaf manuscripts to preserve these potentially priceless records. machine readable text conversion should be done on these images for the easy retrieval and processing. A comparative study of deep learning models for recognizing palm-leaf Malayalam characters (PLMC) was conducted in this work. Characters were segmented directly from palm leaves using the contour method. Various CNN architectures were employed to recognize these segmented characters, namely, VGG16, LeNet, MobileNet, Resnet152v2, VGG19, and DenseNet121. Additionally, a custom CNN architecture was developed and experimented with. It was observed that the custom CNN architecture provides the best recognition accuracy at 96%.

关键词： Deep learning Image segmentation Handwriting recognition Computational modeling asia Computer architecture Organizations

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IISA 2022 Chairs Message

13th International Conference on Information, Intelligence, ...

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13th International conference on Information, Intelligence, Systems and Applications, IISA 2022 2022年

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Intelligent Prediction Technology for Production of Tight Oil Based on Data Analysis 13th

Intelligent Prediction Technology for Production of Tight Oi...

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13th International Field Exploration and Development conference, IFEDC 2023

作者： Li, Ning Wu, Xiang-Hong Li, Xin Wang, Zhi-Ping Wang, Yue-Zhong Zhao, Li-Ao Ren, Liang Wang, Hong-Liang Tian, Hong-Yu Ren, Shu-Hang Jiang, Si-Rui Research Center of Artificial Intelligence Research Institute of Petroleum Exploration and Development PetroChina Beijing China Artificial Intelligence Technology R&D Center for Exploration and Development CNPC Beijing100083 China Department of Asia-Pacific E&P Research Institute of Petroleum Exploration and Development PetroChina Beijing China

ISBN: (纸本)9789819702718

China is rich in tight oil resources, with a wide distribution range and a large amount of resources, making it one of the key areas for strategic replacement of future oil reserves and production. In response to issues such as strong heterogeneity of terrestrial tight oil reservoirs, difficulty in drilling high-quality oil layers, large production differences, and unclear main control factors for production capacity, a detailed analysis of dynamic and static data of production wells was conducted to analyze production performance and decline patterns. Production wells were classified according to production characteristics, and development indicators at different stages were statistically analyzed based on actual production days. Using a combination of principal component analysis and Pearson correlation coefficient, based on multiple dynamic and static data such as geological factors, fracturing factors, and development factors, and analyzing the correlation between different single and combined factors and cumulative oil production at different stages, the main control factors for different production stages of tight oil were obtained. A production capacity prediction model for tight oil fracturing horizontal wells was established based on machine learning intelligent algorithms, A production capacity evaluation and prediction technology for tight oil fracturing horizontal wells has been developed. By comparing with actual production data, the accuracy of the predicted results can meet production needs, providing a strong technical foundation for precise prediction and guidance of tight oil production in China. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Quality control

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Mapping Aquaculture Ponds for the Coastal Zone of asia with Sentinel-1 and Sentinel-2 Time Series

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REMOTE SENSING 2022年第1期14卷 153-153页

作者： Ottinger, Marco Bachofer, Felix Huth, Juliane Kuenzer, Claudia German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD D-82234 Wessling Germany Univ Wurzburg Inst Geog & Geol Dept Remote Sensing D-97074 Wurzburg Germany

asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.

关键词： aquaculture asia Earth observation ponds coastal zone Sentinel-1 SAR time series

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Personality Prediction of Social Network Users using LSTM based Sentiment Analysis 1

Personality Prediction of Social Network Users using LSTM ba...

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International conference on Smart Technologies and Systems for Next Generation computing (ICSTSN)

作者： Jaysundara, Avishka De Silva, Dimanthinie Kumarawadu, Priyantha Asia Pacific Inst Informat Technol Sch Comp Colombo Sri Lanka

ISBN: (纸本)9781665421119;9781665421126

The rapid increase in posting personal information on social media has been noticed over last two years especially due to heavy use of smart phones and restrictions imposed on face-to-face meeting due to the current Covid-19 pandemic. Often, user information and user status updates on social media platforms provide valuable insights in determining personality traits. Scraping and analyzing this social media content and accurate prediction of user personality bring many benefits for enterprises including delivering personalized services, product recommendation and improving performance of recruitment system. This study was carried out based on digital footprints left on social media and the Myers-Briggs Type Indicator (MBTI), one of the most reliable personality inventories, was used to identify and predict the personality traits in mobile applications. Long Short-Term memory (LSTM) architecture-based sentiment classifier was proposed in this paper to predict personality and obtained a convincing model performance with overall 78% accuracy for personality traits.

关键词： Personality prediction Social Media Footprints machine learning Natural Language Processing LSTM

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Fitness dependent optimizer with neural networks for COVID-19 patients

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Computer Methods and Programs in Biomedicine Update 2023年 3卷 100090页

作者： Abdulkhaleq, Maryam T. Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Department of Computer Science and Engineering University of Kurdistan Hewler KRI Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani KRI Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani KRI Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Information Technology Department Asia Pacific International College (APIC) Sydney Australia Singidunum University Danijelova 32 Belgrade 11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India

The Coronavirus, known as COVID-19, which appeared in 2019 in China, has significantly affected the global health and become a huge burden on health institutions all over the world. These effects are continuing today. One strategy for limiting the virus's transmission is to have an early diagnosis of suspected cases and take appropriate measures before the disease spreads further. This work aims to diagnose and show the probability of getting infected by the disease according to textual clinical data. In this work, we used five machine learning techniques (GWO_MLP, GWO_CMLP, MGWO_MLP, FDO_MLP, FDO_CMLP) all of which aim to classify Covid-19 patients into two categories (Positive and Negative). Experiments showed promising results for all used models. The applied methods showed very similar performance, typically in terms of accuracy. However, in each tested dataset, FDO_MLP and FDO_CMLP produced the best results with 100% accuracy. The other models' results varied from one experiment to the other. It is concluded that the models on which the FDO algorithm was used as a learning algorithm had the possibility of obtaining higher accuracy. However, it is found that FDO has the longest runtime compared to the other algorithms. The link to the Covid 19 models is found here: https://***/Tarik4Rashid4/covid19models © 2022

关键词： COVID 19 FDO Fitness dependent optimizer machine learning Swarm intelligence

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