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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Manimegalai, R. Arawind, S. Jegan, G V Sri Rajiv Gomathi, B. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

Weather Forecasting is the application of AI to predict the state of the atmosphere for a given location. Earlier, weather forecasting methods usually relied on observed patterns of events. Our ancestors predicted the next day weather based on the happenings of the previous day evening. However, those intuitive methods and predictions are not reliable. This paper depicts the design and implementation of an application for weather forecasting and visualization using Augmented Reality (AR), which can forecast climatic conditions, namely, such as rain, snow, sun, wind, and hail. This work deals with various real-world weather types and how they could be simulated using a mobile augmented reality system. Users can move freely inside the real world without limitations, experiencing the developed augmented objects. A visual change of the augmented reality weather conditions can be used as a supplement to train the simulations for search and rescue teams during catastrophic disasters. © 2023 IEEE.

关键词： Visualization

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Design and Development of Offline Navigation System (ONS) Using the Smart Phone Sensors

Design and Development of Offline Navigation System (ONS) Us...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Varadam, Deepak Agrawal, Yash Singh Rathore, Sumit Yashwanth Naik, Sugali Verma, Yash Pareta, Vishal M S Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India M S Ramaiah University of Applied Sciences Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

An Offline Navigation system to navigate the user without any connectivity is implemented by using smartphone sensors. The implementation of the system is carried out by extracting the relevant data from the smartphone sensors and discovering some meaningful insights from it. The offline navigation system is completely independent of GPS and any network connectivity such as Wi-Fi, Cellular Network, Bluetooth, etc. The system uses the Accelerometer sensor, Gyroscope sensor, and Magnetic sensor which are already present in the smartphone. The sensor fusion concept is used to combine the data from all the sensors and fuse it to give the device's orientation. The accelerometer sensor provides the acceleration of the device in all three directions. Similarly, the gyroscope sensor provides the angular velocity of the device, and the magnetometer sensor provides the magnetic fields. The data from all three sensors are combined to calculate the orientation angle such as pitch, roll, yaw, and azimuth of the device. The filtering algorithm used is known as Kalman Filter. It fuses the data from different sensors which results in less error on the combined data. The angles then use the position estimation algorithm and step detection algorithm, to find the next step and the coordinates of the device to track the user. The algorithm makes use of the previously determined coordinates of the device. © 2023 IEEE.

关键词： Accelerometers

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Brain Tumor Identification and Classification Using Enhanced Convolution Neural Network 5

Brain Tumor Identification and Classification Using Enhanced...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Raju, J. Anand, Krteen Krishna Kumar, D. Sivaganesan, D. Psg Institue of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350321425

Brain tumor identification and classification play a crucial role in medical imaging. Accurately identifying tumors is a challenging task that often requires highly skilled and experienced doctors. Manual identification is prone to errors, leading to the need for automated solutions. This study aims to utilize advancements in Machine Learning (ML) and Artificial Intelligence (AI) to develop an automated brain tumor identification and classification system. The primary objective is to predict the type of tumor accurately. This study employs a deep neural network architectures specifically Convolutional Neural Network (CNN) and other pre trained deep learning models to find the optimal solution that provides a high degree of accuracy in detecting cancerous cells in the brain. In this study, advanced ML and AI techniques are used to perform brain tumor classification. The study utilizes various deep CNN architectures and methods like random cropping to improve training accuracies. Proposed CNN 7x7 model demonstrates remarkable performance in accurately classifying brain tumors. It achieves a high degree of accuracy in detecting the presence of cancerous cells, surpassing manual identification methods and the accuracies of other pre trained models. The automated brain tumor classification system presented in this study has the potential to streamline the diagnosis and treatment process, significantly reducing the reliance on healthcare systems. By reducing the cost and time required for brain tumor diagnosis, it improves patient access to care. Future research can further enhance the proposed methodology and its applications in medical diagnosis. © 2023 IEEE.

关键词： Medical imaging

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PharmaSafe-Blockchain-Based Counterfeit Detection in the Pharmaceutical Sector

PharmaSafe-Blockchain-Based Counterfeit Detection in the Pha...

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Sivaganesan, D. Kareshmmaa, A.S. Sharan Shankar, A.K. Anitha Petricia, B. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335835

The pharmaceutical industry's crucial task is to look after the medicines while they are being transported, by ensuring ideal storage conditions and keeping track of the complete medications sent to prevent risky scenarios. These three classifications are used by WHO: falsified, substandard, and unregistered/unlicensed. The need for sophisticated tools to identify fabricated pharmaceuticals will rise as criminals grow craftier. To identify phoney and substandard drugs, new technologies are continually being developed. A substantial lack of transparency in the current system makes it difficult for patients to understand the cost of acquired medications, making counterfeit drugs a prime issue. Moreover, if there are accusations of illegal or unethical behaviours, it could be difficult to look into supply chain tampering. The blockchain technology was employed. Two parties' transactions can be efficiently and permanently recorded using an open, distributed ledger like blockchain. Blockchains are transparent, decentralized, distributed, and unchangeable, which makes them an ideal solution for the problem of fake medications. This study proposes a mechanism for the safe distribution of medications throughout the whole supply chain. Each product in the chain can be moved between chain associates who have been confirmed using an event request-response approach. A product may be traced back to its source thanks to smart contracts, which are used to record all transactions between entities onto the blockchain. A DApp is made using the React Framework. On a local blockchain that Ganache provided, the smart contracts were put into action. The blockchain connection between the DApp and *** is made possible by the Truffle framework. The outcomes of the studies show that our method is practical and marginally more secure than existing solutions. © 2023 IEEE.

关键词： Smart contract

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Improve Harris Hawkes optimizer algorithm via Laplace crossover

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2057-2072页

作者： Nasab, Seyed Taha Mousavi Abualigah, Laith Department of Applied Mathematics Iran University of Science and Technology Tehran Iran Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Nowadays, the speed of solving optimization problems by increasing various issues and the number of variables is critical. The Harris Hawk optimization method is a brand-new, intelligent system that resolves optimization issues by mathematically simulating the natural behavior of hawks. In this study, the Harris Hawks optimization method and the Laplace crossover operator are merged, and a new enhanced Harris Hawks algorithm (LX-HHO) based on the Laplace optimization algorithm is suggested. The purpose of improving this algorithm is to increase the convergence speed while maintaining good accuracy in achieving the optimal solution. The results of the proposed method on the test functions demonstrate that the algorithm has achieved faster convergence than the original version of the Harris Hawks algorithm and other meta-heuristic algorithms. This is due to the algorithm's accuracy in obtaining the best answer in these functions and to a reduction in the evaluation of the optimization function. Also, comparing and evaluating the proposed algorithm with other algorithms by Friedman and MAE test shows the superiority of the LX-HHO algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Optimization

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HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

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Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Microbiofuels, and Advanced Sensors for Oceanic Discovery

Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Micro...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Chouhan, Sandeep Sandhu, Ramandeep Yadav, Om Prakash Islam, Sarder M. N. Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India 2 Department of Applied Information Melbourne Australia

ISBN: (纸本)9798331513894

The ocean's depths are among the most enigmatic regions on Earth, with geographical features, temperatures, and marine life remaining widely unexplored. The traditional forms of exploration confront water pressure, cold-temperature conditions, and a lack of energy resources. This research suggests a novel apparatus, that integrates quantum technology, artificial intelligence (AI), and cutting-edge sensors to address these issues and enhance ocean floor mapping, gas detection and element *** energy apparatus of the device is central to ensuring its functionality for a prolonged period. It employs thermoelectric generators exploiting the temperature difference between the cold waters located at depth and warmer surface layers and the microbial fuel cells (MFCs) that convert organic materials in ocean sediments into electricity. This hybrid energy combined cycle, therefore sustainable operation without external power *** sensors such as quantum-enhanced spectroscopy and mass spectrometers will be used for the detection and analysis of gases, trace elements and organic compounds. The balanced engineering systems integrated into the automated equipment will be used with the power distribution control and information processing in the real-time mode performed by the quantum neural networks (QNN). The system's adaptability, and accuracy and that system may function unattended for fairly long periods, this system presents an unprecedented device that should change the course of deep-sea studies as it will provide access to previously unreachable areas. © 2025 IEEE.

关键词： Microbial fuel cells

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An Improved Sparrow Search Algorithm for Node Localization in WSN

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computers, Materials & Continua 2022年第4期71卷 2037-2051页

作者： RThenmozhi Abdul Wahid Nasir Vijaya Krishna Sonthi TAvudaiappan Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Computer Science and Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics Communication and Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia Department of Computer Science and Engineering K.Ramakrishnan College of engineeringTiruchirappalli621112India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge in WSN is node localization which plays an important role in data gathering *** GPS is expensive and inaccurate in indoor regions,effective node localization techniques are *** major intention of localization is for determining the place of node in short period with minimum *** achieve this,bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional *** paper introduces a new Sparrow Search Algorithm with Doppler Effect(SSA-DE)for Node Localization in Wireless *** SSA is generally stimulated by the group wisdom,foraging,and anti-predation behaviors of ***,the Doppler Effect is incorporated into the SSA to further improve the node localization *** addition,the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness *** presented SSA-DE model is implanted in MATLAB *** extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging *** attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.

关键词： Localization wireless networks

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Assessment of processing time and measurement accuracy of different phase compensation methods in Quantitative Phase Imaging via Digital Holographic Microscopy applied to Biological Specimens Holographic Microscopy

Assessment of processing time and measurement accuracy of di...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2024 - Part of Optica Imaging Congress

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied optics group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering University of Massachusetts DartmouthMA United States

Six different methods for phase compensation in Digital Holographic Microscopy are compared using a calibrated test target and a Toxocara canis larva sample regarding processing time, measurement accuracy, and usefuln...

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