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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xiang, Chong-Yang He, Jun-Yan Cheng, Zhi-Qi Wu, Xiao Hua, Xian-Sheng School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Institute for Intelligent Computing Alibaba Group Shenzhen China School of Engineering and Technology University of Washington TacomaWA United States College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS as a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Investigating the Role of Applied Cryptography in Digital Forensics 5th

Investigating the Role of Applied Cryptography in Digital Fo...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Nachappa, M.N. Negi, Bichitra Singh Dakhole, Dipali Khushalrao Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the position of cryptography in virtual forensic evaluation. It aims to discover the one-of-a-kind sorts of cryptographic techniques available to investigators and to talk about how they may be applied to beautify the accuracy of digital forensic work. It also considers the character of cryptographic algorithms and their relative strengths and weaknesses, as well as the numerous applications of cryptographic software and devices in digital forensics. The paper then argues that using cryptography in digital forensic analysis can toughen virtual forensic effects by introducing additional safety features, allowing encrypted facts to be reliably recovered and investigated, and presenting a further degree of warranty while proof is given. It additionally addresses the ethical concerns that must be taken into consideration by virtual forensic investigators during the usage of cryptography in their work. Eventually, it identifies numerous avenues for future studies into using cryptography and its ability applications in virtual forensics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital forensics

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Enhancement of High Angular Resolution Diffusion-Weighted Imaging Through Parallel Imaging and Motion Correction 5th

Enhancement of High Angular Resolution Diffusion-Weighted Im...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Sandhya, Prajapati Sreejith, V. Alam, Intekhab Haripriya Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Excessive Angular decision Diffusion-Weighted Imaging (HARDI) is an effective imaging modality that produces high decision pictures of fiber tracts within the brain. Reconstruction of those pictures calls for a huge quantity of computational power and reminiscence, because it acquires appreciably extra diffusion weighted photographs than the conventional diffusion-weighted imaging (DWI) strategies. It makes use of multiple receiver coils in parallel, with every coil measuring the same records set. MC improves the accuracy of HARDI reconstruction by using correcting for motion artifacts. The application of PI and MC along with HARDI allows high-decision reconstruction of fiber tracts inside the mind, with stepped forward sign-to-noise ratio and decreased test instances. When used together, PI and MC also can improve the accuracy of HARDI tractography, providing extra designated records on fiber tract micros. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： measuring movement reconstruction

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QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

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Investigating the Advantages and Limitations of Math Modeling for Wireless Sensor Networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the blessings and obstacles of math modeling for wireless Sensor Networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. This paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless Sensor Networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling Sensor simulation

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Quantum-Blockchain Healthcare System for Invasive and No-Invasive-IoMT Data 20th

Quantum-Blockchain Healthcare System for Invasive and No-I...

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20th International Conference on Mobile Web and Intelligent Information Systems, MobiWIS 2024

作者： Grønli, Tor Morten Lakhan, Abdullah Younas, Muhammad School of Economics Innovations and Technology Kristiania University College Oslo Norway School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom

ISBN: (纸本)9783031680045

These days, the Internet of Medical Things (IoMT) is a new paradigm where different bio-sensors, such as invasive and non-invasive, generate data to monitor the healthcare of the human body. However, the IoMT application consisted of sensor data (e.g., pacemaker heartbeat detection, blood pressure monitoring, and ingestible gas). These sensor data must be connected to the remote server for decision-making and analysis. Therefore, security and processing time are key issues for IoMT applications in distributed fog cloud networks. This paper presents the Quantum-Blockchain Healthcare System for Invasive and Non-Invasive IoMT Data. The paper has dual objectives: to minimize the processing time and security risk of IoMT data in distributed fog cloud networks and to design a novel quantum blockchain for IoMT application. In this paper, we present quantum key distribution blockchain scheduling (QKDBS), which consists of local scheduling of sensor data and remote scheduling with the quantum blockchain key distribution in the system. Simulations show that QKDBS works better than any other blockchain or quantum method for running IoMT applications on network nodes that are spread out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Pacemakers

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless Sensor Networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

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SN Computer Science 2023年第1期4卷 72页

作者： Theerthagiri, Prasannavenkatesan Ruby, A. Usha Vidya, J. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Bengaluru India School of Computing Science and Engineering VIT Bhopal University Bhopal India

Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. This paper investigates traditional classification algorithms and neural network-based machine learning for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false-positive and false-negative rates with the highest area under the curve of 86%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification of diabetes Diabetes prediction Machine learning algorithm MLP

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Assessments of Channel Coding Performance in Data Compression and Source Coding 5th

Assessments of Channel Coding Performance in Data Compressio...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Bargavi, Manju Panwar, Rahul Shyam, Gopal Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Channel coding is a technique used to boom the performance and reliability of records transmission. It is able to be used for facts compression and source coding, that are the procedures of reducing the amount of records that desires to be despatched across a *** of channel coding performance can be performed with the aid of analyzing the performance of the records compression and source coding techniques. This can be executed thru diverse measures, such as compression ratio, supply coding charge, mistakes detection and correction, and bit blunders rate. With the intention to appropriately verify the performance of the channel coding approach, all of these measures must be considered. The aim of channel coding is to reap the highest viable fee of data compression with minimal errors detection and correction, and bit blunders rates as little as possible. This guarantees that the records is transmitted securely and efficaciously. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coding errors

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Designing Low-Power Industrial Networks with the Internet of Things 5th

Designing Low-Power Industrial Networks with the Internet of...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kannagi, A. Ansari, Tabish Nasurudeen Ahamed, N. Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The Internet of Things (IoT) revolution is evolving conventional commercial networks to fulfill the demands of present-day technological advancements. Low-electricity business networks are becoming increasingly vital to allow a green communique between linked systems and remote monitoring and management of gadgets. Designing and deploying low-electricity networks for business programs brings many demanding situations, which include energy control, conversation latency, and cease-to-give-up safety. This summary will offer a top-level view of the layout issues while implementing IoT and occasional-power business networks, in addition to the capability of high-quality practices for ensuring low-strength, comfortable, and reliable communications among connected devices. Furthermore, IoT techniques for localizing network accessibility, improving facts accuracy, and boosting community performance may be discussed. Subsequently, some of the trendy trends and trends in the industrial networking area could be highlighted. Low-power business networks are getting increasingly important to allow the green conversation of connected structures and remote tracking of devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Capability Management Strength

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