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19th Orissa Information technology Society International Conference on Information technology, OCIT 2021

作者： Nayak, Manas Ranjan Parida, Ajaya Kumar Pattnayak, Parthasarathi Khan, Asif Uddin School of Computer Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Application KIIT Deemed To Be University Bhubaneswar India Silicon Institute of Technology Department of Computer Science Engineering Bhubaneswar India

ISBN: (纸本)9781665416641

The outcomes from Perceptual watermarking attain full benefits of Human Visual System (HVS). Saliency and Phase Congruency (PC) use these benefits by categorizing various perceptual regions of an image. Saliency gives the low perceptual region of an image where Phase Congruency identifies the corner points of an image. Hiding information at this low perceptual region of an image gives better imperceptibility. In this proposed method, bit depth images are generated from Saliency and Phase Congruency which are used to create hiding capacity map for inserting watermark data. Low perceptually visible pixels of Hiding Capacity Map are used for better hiding locations. Watermark information are inserted into the cover image by the help of a LSB matching technique where insertion and extraction are carried out using pair of pixels. The experimental result ensures better Imperceptibility and Robustness with superior data hiding Capacity. © 2021 IEEE.

关键词： Pixels

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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Improving the Quality of P4AI mobile Apps through User Interface Results and User Experience Surveys

Improving the Quality of P4AI Mobile Apps through User Inter...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Yasinta Indrianti Sasmoko Sonya Rapinta Manalu Lukas Kurniawan Entrepreneurship Department Research Interest Group in Education Technology Agung Podomoro University Jakarta Indonesia Primary Teacher Education Department Faculty of Humanities Research Interest Group in Education Technology Bina Nusantara University Jakarta Indonesia Mobile application & Technology School of Computer Science Research Interest Group in Education Technology Bina Nusantara University Jakarta Indonesia Computer Science School of Computer Science Research Interest Group in Education Technology Bina Nusantara University Jakarta Indonesia

The Profiling For Aptitude Inventory (P4AI) application is an aptitude test developed to determine the capacity of students’ interests and talents. The aptitude profile will help students in setting goals and chosen academic fields so that they can support and predict student success in the future. Profiling For Aptitude Inventory (P4AI) application was created using Flutter framework as the frontend and Flask framework as the backend. This research aims to analyze the results of the user interface and user experience to improve application quality. Based on the analysis results of the UI/UX evaluation survey of the P4AI application, it can be concluded that users tend to agree on the attractiveness of the appearance of the P4AI application. However, there are several indicators and items that need to be improved and developed to increase user satisfaction with the appearance and flow of the application. Therefore, changes have been made to the application to improve the quality of application use.

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mobile application to track people in covid19 monitoring and patients under covid19 supervision 2

Mobile application to track people in covid19 monitoring and...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Chrysler, A. Warnars, H.L.H.S. Utomo, W.H. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients. © Published under licence by IOP Publishing Ltd.

关键词： mobile computing

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An algorithm for multi-armed bandit based on variance change sensitivity

Engineering Research Express

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Engineering Research Express 2024年第2期6卷 025209-025209页

作者： Zhu, Canxin Yang, Jingmin Zhang, Wenjie Zheng, Yifeng School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology Taipei106344 Taiwan

The Multi-Arm Bandit problem is becoming increasingly popular as it enables real-world sequential decision making across application domains, including clinical trials, recommender systems, and online decision making. The Multi-Arm Bandit problem is a classical problem of exploration and exploitation dilemma in reinforcement learning and it needs to decide on the optimal strategy based on the reward situation of each rocker arm. However, the existing Multi-Arm Bandit algorithms have many shortcomings, such as blind exploration, weak generalization ability and endless exploration. Aiming at the shortcomings of the existing Multi-Armed Bandit algorithms, this paper proposes a Multi-Armed Bandit algorithm based on variance change sensitivity. The algorithm takes the variance change of reward as a clue, and adjusts the exploration probability by the average variance change of all actions, and selects the action with the largest variance change when exploring. At the same time, in order to reduce the waste of action selection times and maximize the cumulative reward, A parameter N c o n was introduced to record the number of consecutive selections of the same action. The exploration stops when N c o n reaches a certain value. Experiments show that the exploration algorithm can obtain higher reward value and lower regret value in the end. © 2024 IOP Publishing Ltd.

关键词： Reinforcement learning

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Malaria and Pneumonia Disease Prediction Using Deep Learning

Malaria and Pneumonia Disease Prediction Using Deep Learning

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Disruptive Technologies (ICDT), International Conference on

作者： Vijendra Rai Manoj Singhal Bhoopendra Dwivedy Pallavi Jain Ritika Department of Information Technology ABES Institute of Technology Ghaziabad India Department of Information Technology GL Bajaj Institute of Technology and Management Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes.

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic

Empirical Evaluation of Stock Market Prediction System using...

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Intelligent Communication Technologies and Virtual mobile Networks (ICICV), International Conference on

作者： S. Prabakar Ankit Kumar R Jayakarthik D. Venkatesh Pratheeba R S Bhola Khan Finance and Accounting ISBR Business School Bangalore Department of Computer Application Faculty of Science Swami Vivekanand Subharti University Meerut Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Assistant Professor of Commerce School of Commerce and Management Mohan Babu University Tirupati Department of Computer Science and Engineering Velammal Institute of Technology Panchetti Department of Regional Economics MJP Rohilkhand University Bareilly India

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets.

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Density Based Spatial Clustering of applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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computer science, Information technology and Engineering (ICCoSITE), International Conference on

作者： Muhammad Fikri Hasani Yaya Heryadi Yulyani Arifin Lukas Wayan Suparta Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.

关键词： computer science Art Text categorization Clustering algorithms Oral communication Chatbots Classification algorithms

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Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification

Enhanced License Plate Detection using YOLOv8n: An Approach ...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Vishesh Chandok Sudhanshu Maurya Kashish Mirza Ajay Kumar Yadav Sneha Thomas Varghese Rachit Garg Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering Graphic Era Hill University Bhimtal Campus Uttarakhand India Department of Computer Application United Institute of Management Prayagraj India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age.

关键词： YOLO Deep learning Adaptation models Accuracy Surveillance Traffic control Real-time systems Safety Security License plate recognition

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Research on real-time outlier detection over big data streams

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International Journal of computers and applications 2020年第1期42卷 93-101页

作者： Chen, Liangchen Gao, Shu Cao, Xiufeng School of Computer Science and Technology Wuhan University of Technology Wuhan China Department of Computer Application China University of Labor Relations Beijing China

Nowadays technological advances have promoted big data streams common in many applications, including mobile internet applications, internet of things, and industry production process. Outliers should be detected from big data streams in many cases. However, the special characteristics of big data streams, such as transiency, uncertainty, multidimensionality, dynamic distribution, and dynamic relationship make outlier detection more challenging. This paper discusses the key issues, major challenges, and existing most frequently used methods for detecting outliers over big data streams, and then summarizes the directions for further investigation. This research can provide novel theoretical support and technical guidance for outlier detection over big data streams. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Big data

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