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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Farooq, Adil Nain Zukhraf, Syeda Zillay Shafi, Sadman Kamal, Mohsin Chumuang, Narumol University Of Cyprus Department Of Electrical And Computer Engineering Cyprus Penn State University Department Of Electrical Engineering United States National University Of Sciences And Technology School Of Electrical Engineering And Computer Science Pakistan Muban Chombueng Rajabhat University Department Of Digital Media Technology Thailand

ISBN: (纸本)9798350370096

Autonomous drone racing competitions serve as a testing ground for enhancing the perceptual, planning, and control aspects of micro unmanned aerial vehicles (MAVs). This study thoroughly outlines the strategy, methodology, and technical development of a lightweight collision avoidance module for small drones, employed during our participation in the IMAV 2022 Nanocopter AI Challenge held at TU Delft, The Nether-lands. Tailored for nano-sized drones, this competition presented challenges related to agile maneuvers, obstacle avoidance, and navigation in a dynamic indoor arena. We provide detailed insights into our obstacle avoidance algorithm, employing an AI computer vision approach and showcasing its robust performance in dynamic environments. The accuracy of the proposed strategy was verified through simulation testing, highlighting the effectiveness of our approach in a simulated Webots environment across diverse parameter settings. The shared insights aim to contribute significantly to the ongoing evolution of autonomous drone navigation. Moreover, the simulation of our proposed strategy can be seen on the YouTube video link: https://***/HVZPAfyf57M. © 2024 IEEE.

关键词： Drones

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AI-Driven Personalization of E-Therapy Interventions for Anxiety, Stress, and Depression 18

AI-Driven Personalization of E-Therapy Interventions for Anx...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Ramzan, Hafiz Arslan Abdulah, Fatima Ahmad, Muddaber Ramzan, Sadia Ashraf, Munazza School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad Pakistan National University of Computer and Emerging Sciences Department of Computer Science Chiniot Faisalabad Pakistan GIFT University Department of Humanities and Social Sciences Gujranwala Pakistan

ISBN: (纸本)9798331508692

This study explores the use of artificial intelligence (AI) to personalize e-therapy interventions for anxiety, stress, and depression. Leveraging machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees, we analyze a dataset of depression, anxiety, and stress scales responses to develop predictive models that tailor therapeutic interventions. The accuracy and efficacy of proposed models vary, indicating that AI has the potential to improve the level of personalization of mental health therapies. The results indicate that AI-driven techniques can significantly improve the effectiveness and efficiency of e-therapy platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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Blockchain Based Enhanced ERP Transaction Integrity Architecture and PoET Consensus

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computers, Materials & Continua 2022年第1期70卷 1089-1109页

作者： Tehreem Aslam Ayesha Maqbool Maham Akhtar Alina Mirza Muhammad Anees Khan Wazir Zada Khan Shadab Alam Department of Computer Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Management Studies Bahria Business SchoolIslamabad44000Pakistan Department of Computer Science Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computer Science and ITJazan UniversityJazan67714Saudi Arabia

Enterprise Resource Planning(ERP)software is extensively used for the management of business *** offers a system of integrated applications with a shared central *** all business-critical information in a central place raises various issues such as data integrity assurance and a single point of failure,which makes the database *** paper investigates database and Blockchain integration,where the Blockchain network works in synchronization with the database system,and offers a mechanism to validate the transactions and ensure data *** research exists on Blockchain-based solutions for the single point of failure in *** established in our study that for concurrent access control andmonitoring of ERP,private permissioned Blockchain using Proof of Elapsed Time consensus is more *** study also investigated the bottleneck issue of transaction processing rates(TPR)of Blockchain consensus,specifically ERP’s *** paper presents systemarchitecture that integrates Blockchain with an ERP system using an application interface.

关键词： Transaction security enterprise systems blockchain transaction integrity hyperledger sawtooth PoET consensus

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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computer Systems science & engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi Suliman Aladhadh Rehan Ullah Khan Ali Mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

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Public-Key Pseudoentanglement and the Hardness of Learning Ground State Entanglement Structure 39

Public-Key Pseudoentanglement and the Hardness of Learning G...

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39th Computational Complexity Conference, CCC 2024

作者： Bouland, Adam Fefferman, Bill Ghosh, Soumik Metger, Tony Vazirani, Umesh Zhang, Chenyi Zhou, Zixin Department of Computer Science Stanford University CA United States Department of Computer Science University of Chicago IL United States ETH Zürich Switzerland Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA United States

ISBN: (纸本)9783959773317

Given a local Hamiltonian, how difficult is it to determine the entanglement structure of its ground state? We show that this problem is computationally intractable even if one is only trying to decide if the ground state is volume-law vs near area-law entangled. We prove this by constructing strong forms of pseudoentanglement in a public-key setting, where the circuits used to prepare the states are public knowledge. In particular, we construct two families of quantum circuits which produce volume-law vs near area-law entangled states, but nonetheless the classical descriptions of the circuits are indistinguishable under the Learning with Errors (LWE) assumption. Indistinguishability of the circuits then allows us to translate our construction to Hamiltonians. Our work opens new directions in Hamiltonian complexity, for example whether it is difficult to learn certain phases of matter. © Adam Bouland, Bill Fefferman, Soumik Ghosh, Tony Metger, Umesh Vazirani, Chenyi Zhang, and Zixin Zhou.

关键词： Ground state

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pabitha, C. Joshua, K. Paul Pradeep, S. Sophia, S. Sharma, R. Rajesh Prabhu, D. Karthik Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India MAlla Reddy Engineering College for Women Department of Computer Science and Engineering Telangana Secunderabad India Karunya Institute of Technology and Sciences Department of computer Science and Engineering Tamil Nadu Coimbatore India Alliance University Department of Computer Technology Alliance College of Engineering and Design Bangalore India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalaiyam India

ISBN: (纸本)9798331509828

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios;however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41. © 2025 IEEE.

关键词： Mesh generation

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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A Novel Spark-Based Algorithm for Mining Frequent Utility Patterns 6

A Novel Spark-Based Algorithm for Mining Frequent Utility Pa...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Wu, Jimmy Ming-Tai Zhou, Huiying Lin, Jerry Chun-Wei Wang, Ke Liu, Shuo Li, Ranran Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665491259

We are now living in the era of big data thanks to technological innovation. It is very important to discover valuable information in massive data. High utility itemset mining and frequent itemset mining are commonly used techniques in data mining, but they only consider a single factor, ignoring some frequent and efficient itemsets. So as to mine more valuable information, a method for mining skyline frequent utility patterns is proposed. Facing multi-dimensional and large-scale datasets, how to quickly find skyline points is a new challenge. In this article, a parallel algorithm is proposed within the framework of Spark, using Transformations and Actions operators to mine skyline patterns. Through the analysis of experimental data, it is proved that the proposed method has high computing efficiency and high search efficiency, and can effectively mine skyline frequent utility itemsets. © 2023 IEEE.

关键词： Large dataset

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IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO Communications 18

IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanila, Kunnathully Sadanandan Rajamohan, Neelakandan Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Institute of Technology School of Electrical Sciences Goa India

ISBN: (纸本)9798350391725

In this paper, we propose an intelligent reflecting surface (IRS)-assisted hybrid transmit-receive spatial modulation (HSM) for full-duplex (FD) multi-input multi-output communication, referred to as FD-IRS-HSM. In the proposed FD-IRS-HSM, the FD communication between two users is accomplished with a dedicated IRS for each user. In addition to the conventional data transfer, transmit and receive spatial modulations are simultaneously employed in the transmission by tuning the reflecting elements in the IRS. Further, we characterize the self-interference power, present the theoretical analysis for ergodic capacity and outage probability for the proposed system, and substantiate it with simulation results. We also compare the proposed FD-IRSHSM with closely related works in the literature and demonstrate the outperformance of our proposed system in terms of higher transmission rate and greater flexibility. © 2024 IEEE.

关键词： Signal modulation

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