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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S. Kiruthiga C. Anbuananth V. Dhilipkumar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu India Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Deep learning mechanisms allow computers to solve complex real-time problems with complex neural networks, it employs a vital role in health sector, for assisting the medical practitioners with quick and accurate decision making. Various studies on this research area proves that deep learning approaches are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain stream. Accurate and timely detection is most important and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the real-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for accurate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection.

关键词： Deep learning Surveys Accuracy Federated learning Transfer learning Stroke (medical condition) Feature extraction Mathematical models Paralysis Faces

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Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns 1

Hybrid Multi-User Based Cloud Data Security for Medical Deci...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Gupta, Manish Khan, Ihtiram Raza Gomathy, B. Samal, Ansuman Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Department of Computer Science Jamia Hamdard Delhi India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Faculty of Hospitality and Tourism Management Siksha O Anusandhan Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9781607685395

Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, these clouds based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phase's i.e data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns. © The Electrochemical Society

关键词： Cloud Computing Hybrid Multi Model Medical Patterns

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Building an Intelligent Brain Tumor System using Magnetic Resonance Imaging

Building an Intelligent Brain Tumor System using Magnetic Re...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： B. Vinay Kumar Y. Ayyappa Bandaru Kanaka Aparna Bheemineni Ravi Kiran Bandi Naga Gopala Krishna Eda Kavya Faculty Computer Science and Engineering PVP Siddhartha Institute of Technology Computer Science and Engineering PVP Siddhartha Institute of Technology

Detecting Brain Tumor using Magnetic Resonance Imaging takes lot more time in the medical field than computerized analysis. The early prediction of brain tumor helps the physicians to treat the disease and analyze the condition of the patient. According to the study, Artificial Intelligence (AI) and neural networks have improved the accuracy of disease analysis and detection. Brain Tumor occurs as the rapid growth of uncontrollable cells. For patients with tumor, accurate and reliable projections using deep learning and transfer learning can be quite helpful for diagnosis which can be used for therapy planning through the outcome. By using Convolutional Neural Network with a sequential model, the given input of the images is stacked sequentially in the layer of stacks. Medical imaging is used to extract and analyze quantitative imaging features because it can offer radiographic phenotypes of tumor. Clinical information, such as patient age, resection status and examining the reports, doctors can stick to a therapy process according to the patient. Major challenges faced by the existing system is the images are more susceptible to noise and other environmental disturbance. Therefore, it becomes difficult for doctors to determine the tumor and its causes. Moreover, different algorithms are used to detect the tumor from the image but the complex architecture and requirement of high computational features make the existing system more complicated, here the main objective is to build a brain tumor system, which can be run on any simple system and to make sure it is easy to use for researchers and practitioners.

关键词： Computational modeling Magnetic resonance imaging Medical treatment computer architecture Predictive models Brain modeling Feature extraction

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An Examination of Cybersecurity Threats and Authentication Systems 3

An Examination of Cybersecurity Threats and Authentication S...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shakeel, Mohammed Lakshmana Rao, Chintala Shyam Prasad, T. Alam, Tanweer Rawat, Navneet Kavitha, R. Invertis University Faculty of Computer Applications Bareilly India Deemed to Be University School of Law Gitam Visakhapatnam India Anurag University Department of Cse Telangana Hyderabad India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350399264

Each and every nation in the globe has very crucial infrastructures that offer crucial services like internet activity, energy, banking and finance, crucial public services, transportation, and water management. For vital infrastructure based on sectors, each nation has a unique strategy. As IoT-based solutions proliferate, these crucial infrastructures now have network and Internet connectivity. Consequently, these crucial systems that are a part of information networks are equally vulnerable to online attacks. It is crucial to recognise the potential cyber-attack types, devise defence strategies, and implement various preventative measures. In the present day, it is critical to protect potential cyberattacks on these vital infrastructures. The most common attacks on crucial infrastructures are looked at in this study, especially those that have recently happened. Security precautions are also discussed with a view to reducing or preventing IP-based intrusions. The Internet of Things is the next step in machine-to-machine connectivity. IoT has made it possible for anything to be online nowadays. IoT also refers to the networking of uniquely identified, pervasive computing devices that have the ability to transmit data over a network without necessarily requiring or machine-to-human interaction. © 2023 IEEE.

关键词： Cybersecurity

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries 4

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Mola, Sebastianus Adi Santoso Widiastuti, Tiwuk Roma, Rosa V. K. Isabela Ohe Karnyoto, Andrea Stevens Pardamean, Bens University of Nusa Cendana Faculty of Science and Engineering Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength-id dictionary to obtain better performance and compare them against independent InSet and SentiStrength-id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength-id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%. © 2024 IEEE.

关键词： Video streaming

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Qattouz, Hazem Almatarneh, Sattam Mansour, Khalid Alazaidah, Raed Bulkrock, Olla Higher Colleges of Technology Computer Information Science Dept Abu Dhabi United Arab Emirates Princess Sumaya University for Technology Software Engineering Dept Amman Jordan Zarqa University Data Science & Artificial Intelligence Dept Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Arab-Open University Bahrain Faculty of Computer Studies Bahrain

ISBN: (纸本)9798331540012

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop 7

Evaluation of Cloud Recovery System for Financial Applicatio...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Lu, Li Lu, Tong Sali, Aduwati School of Microelectronics and Data Science Anhui University of Technology Maanshan243002 China Department of Computer and Communication Systems Engineering Faculty of Engineering Selangor Serdang43400 Malaysia Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350392074

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies. © 2024 IEEE.

关键词： Data integrity

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EASR: Enabling Neural-Enhanced Video Streaming on Mobile Devices with Edge Assistance

EASR: Enabling Neural-Enhanced Video Streaming on Mobile Dev...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Jiahong Xu Miao Hu Qinglin Zhao Di Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou Guangdong China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Neural-enhanced video streaming systems have successfully addressed the challenge of limited network bandwidth by utilizing super-resolution (SR) techniques to enhance video quality. However, mobile users often face constraints in terms of computational resources required for SR operations. To overcome this, the integration of mobile edge computing becomes crucial. The main challenge lies in efficiently allocating GPU resources on the edge server to multiple users as the available GPU resources are typically insufficient to process all video chunks. In this paper, we formulate the problem of an edge server assisting multiple users in selecting bitrate levels and performing SR inference, and prove its NP-hardness. Subsequently, we propose an edge-assisted video streaming framework named Edge-Assisted SR (EASR) for high-definition neural-enhanced video streaming. This framework is built upon the theory of model predictive control (MPC). EASR addresses the variable reward of SR enhancement across different video chunks by making joint decisions on bitrate, SR, and GPU allocation for each chunk to maximize the average quality of experience (QoE) for all users. We evaluate the performance of EASR on four videos and real network traces. Extensive experiments reveal that EASR outperforms other baselines by $18.76 \%$ to $58.37 \%$ in terms of average QoE and 0.005 to 0.017 in terms of structural similarity index (SSIM).

关键词： Performance evaluation Wireless communication Visualization Bit rate Graphics processing units Streaming media Quality of experience

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

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computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab F.J.Alsolami Hani Choudhry Ibrahim Rizqallah Alzahrani Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control Systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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