We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and...
This paper considers multi-view video and audio transmission on ICN (Information-Centric Networking)/CCN (Content-Centric Networking). Routers in ICN/CCN can cache content. Besides, the capacity of routers' caches...
详细信息
ISBN:
(数字)9798350374537
ISBN:
(纸本)9798350374544
This paper considers multi-view video and audio transmission on ICN (Information-Centric Networking)/CCN (Content-Centric Networking). Routers in ICN/CCN can cache content. Besides, the capacity of routers' caches is finite, so various cache control schemes have been proposed to improve cache efficiency. This paper presents and evaluates a new and suitable control scheme for multi-view video and audio transmission. For this purpose, we construct a network environment with Cefore. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience). We then show the effectiveness of the proposed control scheme.
In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to ev...
In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets.
Global health challenges such as skin cancer, which arises from uncontrolled cell growth, pose a significant threat. The most prevalent form of skin cancer manifests in cells throughout the body, notably in the surfac...
详细信息
Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have sinc...
Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.
Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conven...
详细信息
The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transf...
详细信息
ISBN:
(数字)9798331512248
ISBN:
(纸本)9798331512255
The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transformer-based task interoperability framework purposebuilt for energy-efficient fog-enabled IoMT systems. A customized Transformer design utilizing multi-head selfattention allows the framework to deliver timely critical healthcare data processing alongside optimized energy usage. This model demonstrated a 98.7% task interoperability success rate coupled with a 97.5% anomaly detection accuracy that exceeded results from conventional models based in fog environments and the cloud. The framework improved performance through latency reduction between 50% and 70% and energy consumption minimization reached between 25% to 40% thus proving its effectiveness in resource administration. The scalability tests proved steady system functionality when dealing with different device quantities and built-in security features blocked data breaches and had a false positive rate of 2.3% during testing. FogMedX-Transform demonstrates the capability for healthcare transformation through its union of fog computing and deep learning technology integration according to test results.
Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenge...
详细信息
Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.
AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work ...
详细信息
ISBN:
(数字)9798331521349
ISBN:
(纸本)9798331521356
AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work introduces an innovative AI-based solution for obstacle detection in UAVs, leveraging deep learning techniques to enhance precision and environmental awareness. The system’s architecture involves multiple layers, where UAVs first capture high-resolution images that undergo a processing pipeline including data pre-processing, augmentation, and labeling. A key element of this process is the use of Convolutional Neural Networks (CNNs) to train models capable of identifying obstacles across various terrains. To ensure the integrity and security of the data, especially in complex multi-UAV systems, blockchain technology is integrated. Utilizing Distributed Hash Tables (DHTs) and the Interplanetary File System (IPFS), this decentralized system creates a content-addressable database to store and authenticate unalterable records. Experimental analysis demonstrates that this system offers high accuracy in real-time obstacle detection, minimizing false positives and improving UAV safety.
Depression is the most common health problem in the world. According to the World Health Organization (WHO), more than 350 million people are affected by it. There are many causes of depression, like business down and...
详细信息
暂无评论