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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Intelligent Farming: IoT-Powered System for Efficient Soil Condition Analysis 13

Intelligent Farming: IoT-Powered System for Efficient Soil C...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Sharma, Nandini Singh, Shiv Shanker Khenwar, Medha Gupta, Priyanka Gla University Department of Computer Engineering and Application Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Smart farming technology allows farmers to control and analyse variables in real-time, helping them optimize crop growth and manage their operations more efficiently. The use of specialized sensors, data collection tools, and advanced analytics in Internet of Things (IoT) systems is helping to bridge the gap between physical farm conditions and digital monitoring. In this study, we aim to develop a system that monitors soil conditions to support smart farming. The system provides farmers with live data on key environmental factors, such as temperature, humidity, soil moisture, and soil temperature, to improve crop yield and product quality. The IoT setup for this intelligent farming solution includes components like the DHT11 and humidity sensors, a DS18B20 temperature probe, LDR, water pump, and a 12V LED strip, all controlled by a NodeMCU. When the system is activated, it continuously monitors soil and environmental conditions and sends this information to the cloud for real-time access. If the soil moisture falls below a set threshold, the system automatically triggers irrigation. Our experimental results show that this model improves decision-making for managing farming conditions. © 2024 IEEE.

关键词： Smart agriculture

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Spine Disease Detection in Spinal X-Ray Images using Machine Learning 13

Spine Disease Detection in Spinal X-Ray Images using Machine...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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Preserving Information Integrity: Analyzing the Impact of Deepfake Videos on Social Media Trust and Security

Preserving Information Integrity: Analyzing the Impact of De...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sheikh, Mohammed Firdos Alam Bhuyan, Ajatray Swagat Dhiman, Ankita Sharma, Chandni Diya Joshi, Mayurika Ritu Department of Computer Science & Engineering Poornima University Jaipur India Department of Computer Science & Engineering Chandigarh University Punjab India Ambala Haryana Mullana India Department of Computer Science & Engineering Graphic Era Hill University Haldwani India Department of Computer Science & Engineering Chandigarh College of Engineering Chandigarh Group of Colleges Jhanjeri140307 India

ISBN: (纸本)9798350374933

The proliferation of deepfake videos on social media has raised huge issues about disinformation, identity manipulation, and fraud. Advanced AI techniques now enable the creation of pretty convincing fake films, posing threats to character and countrywide security, eroding consider in digital media, and undermining online discourse. This research paper aims to develop a strong deepfake detection set of rules that leverages superior laptop vision and device getting to know strategies to accurately pick out manipulated films on social media. We will look at the unfold of deepfakes throughout social media platforms, examine their effect on on line discourse, and evaluate the effectiveness of our detection set of rules on numerous platforms. Furthermore, we will provide insights and recommendations for social media organizations, policymakers, and users to mitigate the dangers associated with deepfakes. Our last intention is to make contributions to a safer and greater honest on line surroundings with the aid of combating the spread of deepfakes and disinformation. The study will include a comprehensive literature evaluation on deepfake detection and social media vulnerabilities, experiments and analyses on a massive dataset of deepfake and real motion pictures, visualizations and information illustrating the spread and effect of deepfakes, and an assessment of our detection algorithm’s performance the use of metrics such as accuracy, precision, consider, and F1-score. Relevant legal guidelines, rules, and ethical guidelines related to deepfakes and social media may also be cited. © 2024 IEEE.

关键词： artificial intelligence computer vision deepfake detection identity manipulation social media

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Routing with ART: Adaptive Routing for P4 Switches With In-Network Decision Trees

Routing with ART: Adaptive Routing for P4 Switches With In-N...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Angi, Antonino Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecnico di Torino Department of Control and Computer Engineering Italy Saint Louis University Computer Science Department United States

ISBN: (纸本)9798350351255

Recent advances in Machine Learning (ML) brought several advantages also within computer network management. For programmable data planes, however, it is more challenging to benefit from these advantages, given their limited resource capabilities colliding with the complexity of ML models. In this paper, we propose ART, an attempt to simplify ML-based solutions for routing, so that they can "fit", i.e., be executed, on P4 switches. To provide such model simplification, ART relies on efficient knowledge distillation techniques, converting, in particular, Deep Reinforcement Learning (DRL) models into a simpler Decision Tree (DT). Our evaluation results validate the accuracy of the extracted model and the application of the model logic directly into switches with little impact, paving the way for a more reactive data plane programmability via machine learning integration. © 2024 IEEE.

关键词： Adversarial machine learning

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Review of Efficient Load Balancing Technique to Improve QoS Parameters Fog Computing 2nd

Review of Efficient Load Balancing Technique to Improve QoS ...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Sharma, Arti Mahapatra, Rajendra Prasad Sharma, Vineet Kumar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad201204 India Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad201206 India

ISBN: (纸本)9789819734658

Fog networking is an aspect of the IoT (Internet of Things) idea, which sees most of the products used by humans on a daily basis connected to one another. Smart phones, smart health monitoring equipment, as well as other similar technologies are examples. The goal of fog computing (FC) is to lessen the burden on CC. Fog computing advances processing of data, communication, storing and analysis nearer to edge nodes and applications. To alleviate some of the drawbacks of cloud computing, a significant number of apps and services are shifting to fog computing. The main problem in fog computing is how to improve service quality. It is clear to observe that the SNNC and EBS have improved QoS to a great extent. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog computing

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Cost-aware cloud workflow scheduling using DRL and simulated annealing

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Digital Communications and Networks 2024年第6期10卷 1590-1599页

作者： Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng School of Control and Computer Engineering North China Electric Power UniversityBeijing 100026China School of Mathematics Southwest Jiaotong UniversityChengdu 610032China Department of Computer Science University of ReadingReading RG66AHUK

Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

关键词： Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling Workflow

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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