The article presents a study on classifying wind turbine defects using the SqueezeNet neural network. Wind turbines are critical for renewable energy, but defects such as corrosion, erosion, and cracks can significant...
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When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s ba...
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ISBN:
(数字)9798350368499
ISBN:
(纸本)9798350368505
When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application’s *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application’s accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application’s sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics.
Dengue is becoming a burden for society worldwide and become challenge for the world. The main objective of this research paper is to classify dengue at an early stage. The author has adopted a methodology that is div...
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Underwater acoustic sensor network(UASN)refers to a procedure that promotes a broad spectrum of aquatic *** can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution check...
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Underwater acoustic sensor network(UASN)refers to a procedure that promotes a broad spectrum of aquatic *** can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution checking,and disaster *** confronts many difficulties and issues,such as low bandwidth,node movements,propagation delay,3D arrangement,energy limitation,and high-cost production and arrangement costs caused by antagonistic underwater *** wireless sensor networks(UWSNs)are considered a major issue being encountered in energy management because of the limited battery power of their ***,the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of ***,increased transmission power levels result in higher channel interference,thereby increasing packet *** the facts mentioned above,this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in *** contributions of this technique is ***,it uses the adaptive power control mechanism to utilize the sensor nodes’battery and reduce channel interference ***,thresholds are defined to ensure successful ***,clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference ***,mobile sinks are deployed to gather information locally to achieve the previously mentioned *** suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN *** show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.
Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucia...
Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection.
Existing evaluations of entity linking systems often say little about how the system is going to perform for a particular application. There are two fundamental reasons for this. One is that many evaluations only use ...
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TEACHING proposes a distributed, trustworthy AI integrating continuous human feedback, supporting CPSoS application design and deployment. TEACHING envisions an intelligent environment, empowering humans through cyber...
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Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both cus...
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Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha
Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized...
Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset.
This paper proposes a new tool to help preschool children learn the Sinhala alphabet, as it employs the K-Nearest Neighbors algorithm for handwritten Sinhala character recognition and prediction. The interactive platf...
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ISBN:
(数字)9798331543624
ISBN:
(纸本)9798331543631
This paper proposes a new tool to help preschool children learn the Sinhala alphabet, as it employs the K-Nearest Neighbors algorithm for handwritten Sinhala character recognition and prediction. The interactive platform also addresses the challenges that arise from conventional classroom learning systems, such as the lack of quick feedback, one-off learning, and inflexibility for individual learners. A crucial aspect of the Sinhala script is that it contains fifty-six characters, making character recognition particularly tedious for preschool children. The K-Nearest Neighbors algorithm appears to fill that gap impressively, as it can determine the handwriting of individuals and convert it into usable pieces for students. Usability testing indicated that preschoolers achieved a defined accuracy threshold of 90%, while the model's accuracy was 87%. These indicators provide internal evidence of the tool's actual utility in classroom interactions. The present study highlights the cultural relevance of HCI applications for children in early years education and advances the fields of machine learning systems and the preservation of the Sinhala language. The outcomes of this research demonstrate the technical correctness of the solution and its potential usefulness in applying this method to other languages and educational systems.
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