IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation ...
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IOUT (Internet of Underwater Things) relies on underwater acoustic sensors, which have limited resources such as battery power and bandwidth. The exchange of data among these sensors faces challenges like propagation delay, node displacement, and environmental errors, making network maintenance difficult. The objective of this study is to address the energy efficiency and performance issues in IOUT networks by proposing and evaluating an energy-efficient routing protocol called Efficient Cost Wakeup Routing Protocol (ECWRP). To achieve the objective, the study focuses on two key parameters: Cost and Duty Cycle. The Duty Cycle parameter helps in reducing undesirable impacts during underwater communications, improving the performance of the routing protocol. The Cost parameter is utilized to select the most efficient path for data transmission, considering factors such as transmitting power levels. The protocol is applied to a multi-hop mesh-based network. The proposed ECWRP routing protocol is assessed through simulations, demonstrating its superior efficiency compared to the Ride algorithm. By eliminating unnecessary handshaking and optimizing route selection, ECWRP significantly enhances energy efficiency and overall performance within the IoUT network. The study's findings on the enhanced energy efficiency and performance improvements achieved by the ECWRP protocol hold promising implications for the design and optimization of IoUT networks, paving the way for more sustainable and effective communication systems in underwater environments. In conclusion, the study demonstrates the effectiveness of the Efficient Cost Wakeup Routing Protocol (ECWRP) in enhancing energy efficiency and performance in multi-hop mesh-based IoUT networks. The protocol's utilization of the Duty Cycle parameter reduces undesirable impacts, while the Cost parameter enables the selection of the most efficient path for data transmission. The results confirm the superiority of the ECWRP protoc
Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doc...
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Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is *** paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray *** framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head *** employs cyclical learning rates and weighting-based loss calculation *** modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced *** proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet *** is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray *** proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing ***,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant *** results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.
False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these atta...
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False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy;however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their l2-norm. Based on the results, LSTM, CNN obta
In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)a...
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In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.
A multi-secret image sharing (MSIS) scheme facilitates the secure distribution of multiple images among a group of participants. Several MSIS schemes have been proposed with a (n, n) structure that encodes secret...
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Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, a...
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Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computerscience. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri
The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation(SE).Meanwhile,we note tha...
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The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation(SE).Meanwhile,we note that only a fraction of system states fluctuate at the millisecond level and require to be *** such,refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states ***,this is difficult to achieve with conventional SE methods,which generally refresh states of the entire system every 4–5 *** this context,we propose a local hybrid linear SE framework using stream processing,in which synchronized measurements received from phasor measurement units(PMUs),and trigger/timingmode measurements received from remote terminal units(RTUs)are used to update the associated local ***,the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring *** particular,non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE *** timeliness,accuracy,and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-,300-,and 2383-bus systems.
Confidentiality of maintaining the Electronic Health Records of patients is a major concern to both the patient and Doctor. Sharing the data on cloud is one of the most efficient technology infrastructures with extens...
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The increase in global average life expectancy over the past century has been largely attributed to medical science developments. In this study, we demonstrate a polymer-based capacitive pressure sensor for measuring ...
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Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models ...
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