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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： S. Peerbasha Zaid Alsalami Muntather Almusawi B. Sheeba V. Malathy Department of Computer Science Jamal Mohamed College (Autonomous) Affiliated to Bharathidasan University Tiruchirapalli India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Department of Physics New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering SR University Warangal India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

N owadays, the emotion of every individual human is detected according to the user's facial expressions. The rapid growth of technologies in music information retrieval, personalized music recommendation systems remain in the beginning stage only. However, the personalized music suggestions are common, but, recommending songs based on emotions remains a significant challenge. Hence, this research proposes a Deep Learning (DL) based Convolutional Recurrent Neural Network with Content-Based Filtering (CRNN-CBF) model to recommend personalized music based on emotion. At first, the data gathered from Facial Emotion Recognition 2013 (FER2013) dataset and the collected images are preprocessed by median filter, histogram equalization, and a wiener filter. Then, features are extracted by using Linear Binary Patterns (LBP) method and then classified using CRNN, categorizing images into seven distinct types of user emotions. Next, spotify application programming Interface (API) is employed to deliver playlists and finally, CBF is introduced to recommend personalized music according to user interests. From the results, the proposed CRNN-CBF model achieved outstanding results in accuracy 95.73%, precision 94.86%, and recall 96.78% when compared with existing model such as Emotion-Aware Music Recommendation System by using Deep Neural Network (EA-DNN).

关键词： Histograms Wiener filters Recurrent neural networks Accuracy Filtering Social networking (online) programming Feature extraction Recommender systems application programming interfaces

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Optimization of API Aware Malware Detection Using Artificial LSTM Technique

Optimization of API Aware Malware Detection Using Artificial...

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Artificial Intelligence and Quantum Computation-Based Sensor application (ICAIQSA), International Conference on

作者： Mayank Hindka MBA-Information Systems Texas A&M University -Central Texas USA

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The popularity and open-source nature of APIs have led to a significant increase in malware. Malware developers can evade detection methods, reducing the efficiency of current detection techniques. Therefore, it is crucial for security researchers and experts to devise novel and more effective strategies for analyzing existing and emerging threats on API malware. To effectively examine API security, it is essential to broadly focus on malware development, investigate prevention techniques, and analyze existing detection methods. Traditional malware detection methods are labor-intensive. Machine learning, while instrumental in malware classification and detection, faces challenges in dealing with malware that masquerades as legitimate software and uses self-protection methods, thereby degrading the performance of current machine learning-based methods. This paper presents an analysis of the local malicious behavior of malware and introduces an anti-interference detection framework based on API fragments. The framework leverages an LSTM model to classify API fragments and uses ensemble learning to determine the overall classification of the entire API. We also compare the enhanced accuracy and other parameters of the LSTM-based classification with traditional methods and other machine-learning techniques.

关键词： Learning systems Accuracy Quantum computing Prevention and mitigation Predictive models Malware Security Long short term memory Optimization application programming interfaces

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Assessing the Linguistic Design Quality of APIs of Distributed Systems and Microservices

Assessing the Linguistic Design Quality of APIs of Distribut...

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Collaborative Advances in Software and COmputiNg (CASCON), International Conference on

作者： Krishno Dey Hung Cao Francis Palma SE+AI Research Lab Faculty of Computer Science University of New Brunswick NB Canada Analytics Everywhere Lab Faculty of Computer Science University of New Brunswick NB Canada

ISBN: (数字)9798331504830

ISBN: (纸本)9798331515621

APIs or application programming interfaces help distributed systems and microservices to expose their functionalities and serve as a means for communication among them. In contrast to a poorly designed API, a well-designed API is easy for users to understand and use. Thus, APIs with high-quality design are essential both for API providers and client developers. This paper aims to assess the linguistic design quality of APIs in distributed systems and microservices by automatically detecting good and poor design practices, commonly known as patterns and antipatterns, respectively. We rely on syntactic and semantic analyses for automatic assessment of the design quality of APIs using detection heuristics. Syntactic analysis involves analyzing the structure and syntax of the APIs, while semantic analysis involves analyzing API documentation, descriptions, and parameters. We achieved an overall accuracy of more than 93% in detecting patterns and antipatterns. Our detection results also suggest that antipatterns are prevalent in the APIs of distributed systems and microservices. Our findings will assist API developers in identifying poor design practices and improving the design quality of their APIs.

关键词： Accuracy Semantics Microservice architectures Collaboration Documentation Syntactics Linguistics Software application programming interfaces

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A GraphQL API for Product Recommendation in Women's Clothing Based on Deep Learning Analysis of Product Reviews

A GraphQL API for Product Recommendation in Women's Clothing...

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International Multidisciplinary Information Technology and Engineering Conference (IMITEC)

作者： Tinashe Crispen Garidzira Courage Matobobo Software Developer Independent Researcher East London South Africa Business and Application Development Walter Sisulu University East London South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

Automated Product Recommendation (APR) plays a vital role in modern technology by leveraging Artificial Neural Network (ANN) models powered by deep learning techniques. This study focuses on improving consumer decision-making for women's clothing through an APR system that analyzes review text and approval ratings. GraphQL application programming Interface (API) integration facilitates seamless interaction with the recommendation system, allowing efficient querying and retrieval of personalized product recommendations. The developed ANN model showed remarkable effectiveness, achieving an accuracy of 51% in predicting product ratings and an accuracy of 87% in recommending products.

关键词： Deep learning Accuracy Reviews Clothing Artificial neural networks Predictive models Prediction algorithms Servers Recommender systems application programming interfaces

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Development of Weather Forecast application using API

Development of Weather Forecast Application using API

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Shikha Chadha Neha Gupta Rosey Chauhan Department of Computer Science Engineering Sharda University Greater Noida ABES Engineering College Ghaziabad

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This research paper presents the development of a weather forecasting model that incorporates real-time data through application programming interfaces. This model utilises simple algorithms to analyse meteorological parameters from diverse API data sources, aiming to improve forecast accuracy compared to traditional methods. Extensive datasets are used for training and validation, demonstrating the model’s effectiveness in providing more precise predictions. The study highlights the significance of API integration in advancing weather forecasting methodologies and fostering innovation in the field. The model created has yielded highly reliable results with minimal error.

关键词： Training Technological innovation Soft sensors Weather forecasting Predictive models programming Data models Real-time systems Reliability application programming interfaces

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Real Time Network Threat Detection Using Machine Learning and Kafka in Middleware

Real Time Network Threat Detection Using Machine Learning an...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： K. Jaspin Yogesh Shamlin Shinanth J S Srihari Kishore K Computer Science and Engineering St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The increasing complexity of network traffic resulting from internet-based services and data exchanges presents formidable obstacles to real-time analysis and decision-making in the field of network security. A real-time network traffic analysis system was created using Kafka, Redpanda, and machine learning to overcome these difficulties. Flow-based values were collected from CICFlowmeter. To efficiently classify network traffic, these attributes are subsequently examined using a machine learning model that has already been trained. Our integration of a Flask application programming Interface (API) with Kafka guarantees efficient, real-time data handling and processing. Efficient deployment is made possible via Docker, which also makes the system compatible with any existing server infrastructure. Performance analysis was conducted to assess how well the system handles real-time traffic and its accuracy in classifying network data, an accuracy of 99.4% is achieved using the Random Forest Classifier algorithm. The findings show that using Redpanda for Kafka, alongside machine learning, greatly improves the speed and effectiveness of data processing and classification. This approach provides a solid and adaptable solution for enhancing network security.

关键词： Accuracy Telecommunication traffic Network security Ubiquitous computing Solids Real-time systems Threat assessment Servers Random forests application programming interfaces

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FIRE-3DV: Framework-Independent Rendering Engine for 3D Graphics Using Vulkan

FIRE-3DV: Framework-Independent Rendering Engine for 3D Grap...

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IEEE International Conference on Robotic Computing (IRC)

作者： Christopher John Allison Haoying Zhou Adnan Munawar Peter Kazanzides Juan Antonio Barragan Khoury College of Computer Sciences Northeastern University Boston MA USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

Interactive dynamic simulators are an accelerator for developing novel robotic control algorithms and complex systems involving humans and robots. In user training and synthetic data generation applications, high-fidelity visualizations from the simulation are essential. Yet, robotic simulators often limit their rendering algorithms to preserve real-time interaction with the simulation. Advancements in Graphics Processing Units (GPU) enable improved visualization without compromising performance. However, these advancements cannot be fully leveraged in simulation frameworks that use legacy graphics application programming interfaces (API) to interface with the GPU. This paper presents a performance-focused and lightweight rendering engine supporting the modern Vulkan graphics API that can be easily integrated with other simulation frameworks to enhance visualizations. To illustrate the proposed method, our engine is used to modernize the legacy rendering pipeline of the Asynchronous Multi-Body Framework (AMBF), a dynamic simulation framework used extensively for interactive robotics simulation development. This new rendering engine implements graphical features such as physically based rendering (PBR), anti-aliasing, and ray-traced shadows, significantly improving the image fidelity of AMBF. Computational experiments show that the engine can render a simulated scene with over seven million triangles while maintaining GPU computation times within two milliseconds.

关键词： Training Three-dimensional displays Heuristic algorithms Graphics processing units Data visualization Rendering (computer graphics) Robots Engines application programming interfaces Synthetic data

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Emotion detection of social data: APIs comparative study

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HELIYON 2023年第5期9卷 e15926页

作者： Abu-Salih, Bilal Alhabashneh, Mohammad Zhu, Dengya Awajan, Albara Alshamaileh, Yazan Al-Shboul, Bashar Alshraideh, Mohammad Univ Jordan Amman Jordan Curtin Univ Perth Australia Al Balqa Appl Univ Salt Jordan

The development of emotion detection technology has emerged as an efficient possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of various start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparisons to social data. This study compares eight technologies: IBM Watson Natural Language Understanding, ParallelDots, Symanto - Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and Natural Language Processing Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The perfor-mance of these APIs was assessed using the aggregated scores they delivered and the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.

关键词： Emotion detection Emotion analysis application programming interfaces Social emotion analysis Commercial tools Comparative study

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WIP: Towards Cloud-based Wildland Fire Simulation Service

WIP: Towards Cloud-based Wildland Fire Simulation Service

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IEEE International Conference on Service-Oriented System Engineering, SOSE

作者： Xiaolin Hu Mingxi Yan Tony Derado Wei Zhao Bernard Zeigler Doohwan Kim Chungman Seo Department of Computer Science Georgia State University Atlanta GA USA RTSync Corp. Chandler AZ USA

ISBN: (数字)9798331539580

ISBN: (纸本)9798331539597

Wildland fire simulation is a useful tool for studying wildland fires and for developing new technologies to help wildland fire management. This paper presents an effort of developing wildland fire simulation as a service that is accessible to broader users. The developed simulation service supports fire spread simulation, fire suppression simulation, and prescribed fire simulation with dynamic ignitions, and is accessible through a graphic user interface and an application programming interface (API). We present the underlying DEVS-FIRE simulation model, the design of the cloud-based wildland fire simulation service, and some preliminary results.

关键词： Wildfires Service-oriented systems engineering Ignition Dynamic programming application programming interfaces Graphical user interfaces

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API Rate Limit Adoption -- A pattern collection 23

API Rate Limit Adoption -- A pattern collection

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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Souhaila Serbout Amine El Malki Cesare Pautasso Uwe Zdun Software Institute Università della Svizzera italiana Switzerland Faculty of Computer Science Software Architecture Research Group and UniVie Doctoral School Computer Science DoCS University of Vienna Austria Faculty of Computer Science Software Architecture Research Group University of Vienna Austria

ISBN: (纸本)9798400700408

The API Rate Limit pattern controls the rate at which clients make API requests by counting the number of requests in a specified time interval and reacting against abusive clients, in order to protect the limited resources of the API from exhaustion and denial of service attacks. This practice helps service providers to prevent abuse and ensure fair resource allocation, maintain system stability, monitor and control service availability, protect against DDoS attacks In this research paper, we have identified patterns covering the API Rate Limit pattern adoption starting from its documentation to its implementation. Our objective is to elucidate the trade-offs associated with different identified patterns and offer guidance to developers in making informed decisions when choosing the most suitable Rate Limit method, scope, and granularity for their service. By providing a comprehensive overview of how to adopt the Rate Limit pattern, this paper aims to enhance the understanding of how APIs can be designed to facilitate high scalability, security, reliability, and service availability. Furthermore, we present each pattern along with known uses observed in real-world APIs and technologies.

关键词： application programming interfaces Pattern Language Rate Limit

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