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共 24453 条记录，以下是111-120 订阅

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Detection systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review 21

Detection Systems for Distributed Denial-of-Service (DDoS) A...

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21st International Multi-Conference on systems, Signals and Devices, SSD 2024

作者： Adil Nafea, Ahmed Maad Hamdi, Mustafa Abdulhakeem, Baraa Saad Shakir, Ahmed Thair Alsumaidaie, Mustafa S. Ibrahim Shaban, Ali Muwafaq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Artificail Inteligence Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Computer Science Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Information System Iraq College of Applied Sciences University of Anbar Ramadi Dept. of Biophysics Iraq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Information System Iraq

ISBN: (纸本)9798350374131

The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field. © 2024 IEEE.

关键词： Time series analysis

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TurfIt: An Instant Turf Booking Application

TurfIt: An Instant Turf Booking Application

引用

2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Rajeswari, A.M. Tamilselvan, V. Abishek, M.R.P. Dept. of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331512965

As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectiveness in scheduling, and over-bookings, leading to frustrations and operational problems. This paper focuses on those problems by the presentation of an online turf booking application to streamline the booking process. The solution offers real-time updates, automatic payment, and safe transactions based on QR code. The more user-friendly the application is, the more efficient it will be. Firebase would be used for real-time data synchronization and the responsiveness of React interface ensures the smooth and conflict-free bookings. This solution would lead to increasing customer satisfaction as well as increasing the booking rates and effective utilization of sports facilities. © 2025 IEEE.

关键词： Customer satisfaction

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A Novel Deep Ensemble Learning Approach to Predict Eye Diseases from OCT Images 5

A Novel Deep Ensemble Learning Approach to Predict Eye Disea...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Nazim Mia, Md Sajib Uddin, Mohammed Nasir Mahmud, Prince Chandpur Science and Technology University Dept. of Information and Communication Technology Bangladesh Sylhet Engineering College Dept. of Computer Science and Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Chandpur Science and Technology University Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

As the world advances in technology, the classification of eye diseases is thought to have clinical applications since it evaluates and analyzes the results. Optometrists research and diagnose age-related eye problems such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), and Drusen using optical coherence tomography (OCT) of the human eye. Because their symptoms are similar, identifying these diseases is challenging and requires hours of expert analysis. The requirements for eye illness classification should be straightforward in nature, structure, and evaluation. Deep Learning Convolutional Neural Network Classification can create a hierarchical representation of images to differentiate between normal and diseased eye patterns for diagnostic purposes. Therefore, this paper aims to implement and define deep learning to classify and identify eye diseases. Using a four-class classification problem, this work investigates the automatic detection of Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal in Optical Coherence Tomography (OCT) pictures. Multi-transfer learning architecture was used in the suggested deep learning Ensemble classification model. The most incredible accuracy of our proposed Ensemble model for multi- class classification was 83%. © 2023 IEEE.

关键词： Optical tomography

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Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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Financial Health Assessment for Households in Kenya

Financial Health Assessment for Households in Kenya

引用

2024 International Conference on Information and Communication technology for Development for Africa, ICT4DA 2024

作者： Kinyanjui, Samson Lopuran, Felix Kimanga, Peter Mugoh, Edna Kiprotich, Dennis Gikunda, Patrick Dedan Kimathi University of Technology Dept. Computer Science Nyeri Kenya

ISBN: (纸本)9798331527792

The analysis of fiscal position represents significant in the encouragement of economic stability and development specifically to the country like Kenya. This work employs the clustering analysis technique in order to assess the Kenyan households' financial literacy based on a financial dataset. These include transaction details, current incomes, expenditures, savings and investment practices among the clients. This paper makes use of K-means clustering technique to investigate the various clusters of households that possibly exist based on their transactional data and assess the financial characteristics and health profiles of the respective clusters. The data used was gathered from 298 distinct households across 5 counties in Kenya. Using correlation analysis and variance threshold, a feature selection method, which aimed at identifying features depicting the greatest extent of clustering tendency was also created. The clustering resulted into eight groups mainly identifiable by transaction direction, transaction value,transaction family, transaction purpose, and mode of transaction. The findings are useful in forecasting and mitigating potential risks within financial planning and management, and may improve financial wellbeing and sustainability at the household level. The project demonstrates how it is possible to implement machine learning methodologies that provide more valuable information at more efficient assessments of the financial well-being of households in Kenya. © 2024 IEEE.

关键词： Decentralized finance

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Preyanshu Kumar, Suresh Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring. © 2025 IEEE.

关键词： Online systems

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adept.y identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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