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3rd IEEE International Conference on Communications, Information System and computer engineering, CISCE 2021

作者： Ying, RunKai Shou, Yuntao Liu, Chang School of Software Beijing University of Technology Faculty of Information Technology Beijing China School of Computer and Information Engineering Central South University of Forestry and Technology Hunan China School of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9780738112152

How to accurately predict the Dow Jones index is an important issue in quantitative finance. This paper designs an LSTM-AdaBoost(Long Short-Term Memory and Adaptive Boosting) prediction model based on the Dow Jones index's characteristics. The AdaBoost algorithm was used to integrate multiple LSTM weak classifiers. By iteratively updating the weights of each version and combining the strategy, the improved robust classifier was formed, and the prediction for the Dow Jones index was finally obtained. Experimental results have shown that the prediction results obtained by the LSTM-AdaBoost model have been significantly higher than those of the traditional classification model, with an average increase of 43.76 percent in R_square. The establishment of this model provides a theoretical basis for predicting the Dow Jones index and provides ideas for the intersection of finance and computer industries. © 2021 IEEE.

关键词： Adaptive boosting

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The Future of Crime Prevention: Police Case Analysis Using Machine Learning

The Future of Crime Prevention: Police Case Analysis Using M...

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Advancements in Computing (ICAC), International Conference on

作者： Dharsan. R Krishanthini M Traveena C Anubama L Mahaadikara M.D.J.T. Hansika Sanjeevi Shahikala Chandrasiri Department of Computer Science and Software Engineering (CSSE) Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Law enforcement agencies and police departments face challenges in analyzing and preventing accidents and crimes. This research presents a comprehensive machine learning system, “The Future of Crime Prevention: Police Case Analysis,” to enhance public safety. The system comprises components like accident case analysis, crime analysis, case document classification, and crimes against women analysis. It leverages various machine learning techniques to predict accident causes, analyze criminal cases, automate document classification, and cluster crimes against women. The objectives encompass bolstering public safety, enabling proactive decision-making, anticipating future crime rates, and optimizing document analysis. This research strives to provide effective tools for crime and accident prevention, addressing traditional challenges faced by police departments and crime divisions. In an era of advancing technology, the need for innovative solutions to ensure public safety has never been more crucial. Furthermore, it seeks to bridge the gap between law enforcement agencies and cutting-edge technology, revolutionizing the landscape of crime prevention and public safety.

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The effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks

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Journal of Optical Communications 2024年第s1期45卷 s253-s259页

作者： Eid, Mahmoud M. A. Zaki Rashed, Ahmed Nabih Ahammad, Mohd. Sultan Paul, Bikash Kumar Ahmed, Kawsar Department of Electrical Engineering College of Engineering Taif University Taif Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Dhanmondi Sukrabad Dhaka1207 Bangladesh

This study presents the effects of Tx./Rx. pointing errors on the performance efficiency of local area optical wireless communication networks. The received signal power and max Q factor are measured in the presence of vertical-cavity surface-emitting lasers (VCSELs) bias current and modulation current at maximum propagation distance for the wireless network. The signal is enhanced with increasing of bias and modulation peak currents of the VCSEL device. The optimum received power and max. Q factor is also examined at Tx./Rx. the pointing error of 0.1 mrad and propagation reach of 5 km at available bit rates transmission of 10 Gb/s. © 2021 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Surface emitting lasers

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Playing the video games during COVID-19 pandemic and its effects on player's well-being

Playing the video games during COVID-19 pandemic and its eff...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Samar Billi Noaman Amer Ibrahim Liaqat Ali M. W. Iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life 1 .

关键词： COVID-19 Video games Pandemics Games Machine learning Behavioral sciences Security

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems

Robust and Reliable Liveness Detection Models for Facial Rec...

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Frontiers of Information Technology (FIT)

作者： Haris Anjum Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Abu Bakar Siddique Muhammad Imad Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model’s accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system.

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A Three-Stage Cutting Simulation System Based on Mass-Spring Model

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computer Modeling in engineering & sciences 2021年第4期127卷 117-133页

作者： Xiaorui Zhang Jiali Duan Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

The cutting simulation of soft tissue is important in virtual *** includes three major challenges in computation:Soft tissue simulation,collision detection,and handling,as well as soft tissue *** order to address the earlier challenges,we propose a virtual cutting system based on the mass-spring *** this system,MSM is utilized to simulate the soft tissue *** stress is introduced to the model for simulating the shrinking effect of soft tissue in ***,a cylinder-based collision detection method is used to supervise the collision between surgical tools and soft ***,we simulate the cutting operation with a three-stage cutting method with swept volume,B´ezier curve,and an algorithm named shortest distance nodes matching *** order to verify the system performance,we carry out three validation experiments on the proposed system:Cutting accuracy experiment,collision detection validation,and practical cutting *** indicate that our system can well perform the shrinking effect of soft tissue in *** system has fast and accurate collision ***,the system can reconstruct smooth incisions vividly.

关键词： Mass-spring model collision algorithm virtual surgery soft tissue simulation

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Message from program committee

2021 International Conference on Code Quality, ICCQ 2021

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2021 International Conference on Code Quality, ICCQ 2021 2021年 5-5页

作者： Zykov, Sergey V. Software Engineering Department Computer Science Faculty Hse University Moscow Russia

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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Multi-Classification of Depression Levels Based on Blood Biomarkers

Multi-Classification of Depression Levels Based on Blood Bio...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Rahmi Kavak Selma Ayşe Özel Sema Polat Sinan Özler Department of Software Engineering Adana Alparslan Türkeş Science and Technology University Adana Turkey Department of Computer Engineering Çukurova University Adana Turkey Department of Anatomy Faculty of Medicine Çukurova University Adana Turkey Department of Mental and Nervous Diseases The Seyhan State Hospital Adana Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

Depression is a psychiatric condition characterized by a persistent feeling of sadness and diminished interest in significant activities. Diagnosing this health problem is challenging because it relies on several social and physiological factors. The timely identification of depression aids in the prevention of severe outcomes, such as suicide. Early detection of depression levels is crucial to prevent adverse effects and enhance the quality of everyday life. The purpose of this work is to use data preprocessing techniques to create a clean dataset from noisy and incomplete medical data that includes blood biomarker values of patients and then apply data mining techniques to this dataset to predict the degree of depression. Adana Dr. Ekrem Tok Mental Health Hospital supplied the raw data for the study, with consent from the ethics committee. Missing data are completed by filling with the constant value and the most frequent value methods (i.e., 0 and mode values) in the relevant column. The classification of the dataset is performed using AdaBoost, Decision Tree (DT), and Logistic Regression (LR) classifiers, which have been previously used in medical datasets and demonstrated to be effective. The Logistic Regression classifier achieved the highest success rate (Accuracy: 0.541 and weighted F-score: 0.481).

关键词： Logistic regression Ethics Hospitals Prevention and mitigation Mental health Depression Noise measurement Decision trees Medical diagnostic imaging Blood

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Brain sight: a novel system for substituting vision sensor

Brain sight: a novel system for substituting vision sensor

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The 3rd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： N. Y. Elsaeed M. M. Alshaarawy A. E. S. Esmaiel S. E. D. Elmahdy O. Abdelhameed M. M. Fakhry N. A. Ogabih N. M. Ali R. El-Deeb Software Engineering Department Faculty of Computers and Information Mansoura University Mansoura 35516 Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura 35516 Egypt

In a world where nearly everything we do depends on sight; it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers.

关键词： brain computer vision edge detection handicapped aids haptic interfaces image sensors neurophysiology

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