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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Optimized Load Balancing Technique for Software Defined Network

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computers, Materials & Continua 2022年第7期72卷 1409-1426页

作者： Aashish Kumar Darpan Anand Sudan Jha Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Chandigarh UniversityMohaliPunjab140413India School of Computational Sciences CHRIST(Deemed to be University)Ghaziabad201003India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Automotive ICT Convergence Engineering Daegu Catholic UniversityGyeongsan38430Korea

Software-defined networking is one of the progressive and prominent innovations in Information and Communications *** mitigates the issues that our conventional network was ***,traffic data generated by various applications is increasing day by *** addition,as an organization’s digital transformation is accelerated,the amount of information to be processed inside the organization has increased *** might be possible that a Software-Defined Network becomes a bottleneck and *** models have been proposed in the literature to balance the ***,most of the works consider only limited parameters and do not consider controller and transmission media *** loads also contribute to decreasing the performance of Software-Defined *** work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the *** proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network *** simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques.

关键词： SDN software-defined networks load balancing performance enhancement

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Ultrahigh-resolution atomic localization via superposition of standing waves

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Communications in Theoretical Physics 2025年第3期77卷 57-65页

作者： Muhammad Idrees Ahmed S Hendy Zareen A Khan Institute of Nonlinear Physics and Department of Physics Zhejiang Normal UniversityJinhua 321004China Zhejiang Institute of Photoelectronics and Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua 321004China Department of Computational Mathematics and Computer Science Institute of Natural Sciences and MathematicsUral Federal University19 Mira St.Yekaterinburg 620002Russia Department of Mechanics and Mathematics Western Caspian UniversityBaku 1001Azerbaijan Department of Mathematical Sciences College of SciencePrincess Nourah Bint Abdulrahman UniversityPO Box 84428Riyadh 11671Saudi Arabia

In this study we theoretically demonstrate ultrahigh-resolution two-dimensional atomic localization within a three-levelλ-type atomic medium via superposition of asymmetric and symmetric standing wave *** analysis provides an understanding of the precise spatial localization of atomic positions at the atomic level,utilizing advanced theoretical approaches and principles of quantum *** dynamical behavior of a three-level atomic system is thoroughly analyzed using the density matrix formalism within the realm of quantum mechanics.A theoretical approach is constructed to describe the interaction between the system and external fields,specifically a control field and a probe *** absorption spectrum of the probe field is thoroughly examined to clarify the spatial localization of the atom within the proposed configuration.A theoretical investigation found that symmetric and asymmetric superposition phenomena significantly influence the localized peaks within a two-dimensional spatial ***,the emergence of one and two sharp localized peaks was observed within a one-wavelength *** observed notable influences of the intensity of the control field,probe field detuning and decay rates on atomic ***,we have achieved an unprecedented level of ultrahigh resolution and precision in localizing an atom within an area smaller thanλ/35×λ/*** findings hold promise for potential applications in fields such as Bose-Einstein condensation,nanolithography,laser cooling,trapping of neutral atoms and the measurement of center-of-mass wave functions.

关键词： ultrahigh resolution atom localization symmetric and asymmetric superposition probe absorption standing wave fields

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Exploring Multimodal Generative AI for Education through Co-design Workshops with Students 25

Exploring Multimodal Generative AI for Education through Co-...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Prasad, Prajish Balse, Rishabh Balchandani, Dhwani School of Computing and Data Sciences FLAME University Pune India Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400713941

Multimodal large language models (MLLMs) are Generative AI models that take different modalities such as text, audio, and video as input and generate appropriate multimodal output. Since such models will be integrated into future educational tools, a human-centered design approach that takes students' perspectives into account is essential while designing such *** paper describes two co-design workshops which were conducted with 79 student groups to examine how they design and prototype future educational tools integrated with MLLMs. Through various activities in the workshops, students discussed relevant educational problems, created journey maps, storyboards and low fidelity prototypes for their applications, and evaluated their applications based on relevant design principles. We found that students' applications used MLLMs for important learning environment design features such as multimodal content creation, personalization, and feedback. Based on these findings, we discuss future research directions for the design of multimodality in generative AI educational applications. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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Exploring the Effectiveness of Deep Learning in Audio Compression and Restoration 2

Exploring the Effectiveness of Deep Learning in Audio Compre...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Pushparani, S. Rekha, K. Sashi Sivagami, V.M. Usharani, R. Jothi, M. Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology SriperumbudurTamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Tamil Nadu Kattankulathur India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India

ISBN: (纸本)9798350384277

Modern audio processing requires compression and restoration for streaming services and data storage and transmission. This study examines how deep learning can address these difficulties and includes an architecture flow diagram. The suggested complex design has numerous integral stages optimized for audio compression and restoration. The input layer contains raw audio data. The pre-processing module normalizes and extracts features from raw data, frequently using Mel-frequency cepstral coefficients (MFCC) to capture audio characteristics. This module improves data quality by reducing noise and filtering. The architecture's heart is the compression module, where deep learning is used. This module compresses audio data using neural networks, maybe CNNs or RNNs. This compression is quantified by compression ratio and bitrate, with hypothetical values representing audio quality trade-offs. The compression module compacts audio data for storage or transmission. To minimize data size, quantization and encoding are common here. To accurately decompress audio, the decompression module mimics the compression module's architecture. Depending on needs, error correction or quality enhancement layers may be added. Filtering, equalization, and dynamic range compression may be used in the post-processing module to improve audio quality. If needed, a restoration module uses deep learning models to minimize noise and artifacts. This study presents four hypothetical scenarios to demonstrate different results. The scenarios emphasize high compression, balanced approaches, quality prioritization, and low resource utilization. Deep learning approaches can adapt to varied audio processing needs by balancing compression ratio, bitrate, audio quality (measured by PSNR), and restoration accuracy in each case. © 2024 IEEE.

关键词： Audio streaming

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Tensor Multi-Task Learning for Multi-View Representation of 3D Shape

Tensor Multi-Task Learning for Multi-View Representation of ...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Yan Fu, Sujia Qu, Yanyun School of Mathematical Sciences Guizhou Normal University Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Xiamen University Computer Science Department Xiamen China

ISBN: (纸本)9798350303759

Research on 3D representation has sprung up recently. However, most existing view-based methods lack interpretability and few researchers explore to mine high-order correlation for multi-view features aggregate in 3D shape representation. This paper presents a tensor multi-task model learning, T-MTL, which transforms the original multi-view classification problem into constructing a common space in which the multi-view representation can be compared. The proposed method can construct a tensor structure in the classifier weight subspace with multiple viewpoints and use a tensor low-rank constraint and Bregman discrepancy to capture high-order correlations between multi-view images and 3D models. In the classifier weight subspace, our method makes the inter-class divergence larger and the intra-class divergence smaller. Moreover, the proposed model can improve recognition performance further by unifying multiple visual features in a flexible manner. Abundant experiments on these challenging benchmark datasets show that our method achieves comparable results to the deep learning models approach. © 2023 IEEE.

关键词： Tensors

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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A Smart Blockchain Model for Supply Chain Management System 4th

A Smart Blockchain Model for Supply Chain Management System

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Ayo, Femi Emmanuel Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago Iwoye Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819724505

Industry 4.0 is the creation of smart industrial processes. Therefore, supply chain management describes a chain of activities from the purchase of raw materials to the distribution of finished goods. The problems with the existing manual supply chain system include consistency, timeliness, traceability, security, and trust among all supply chain actors. This study presented the integration between a rule-based expert system and blockchain technology called SupplyOnBlock for supply chain management. The rule-based expert system was proposed to create smart industrial processes and blockchain technology to create fast, traceable, and secure supply chain processes. The rule-based expert system is to create a smart individual process in the supply chain while the blockchain is to coordinate the individual smart processes in a network of blockchains. The proposed SupplyOnBlock model for supply chain management is designed to use the public key cryptographic hash function for encryption of key processes to avoid non-repudiation at any point in the SupplyOnBlock. The information to be transferred on the block is first hashed into a value to compress its volume then the public key cryptography is used to encode and decode the hashed information. The proposed encryption scheme should be able to validate the legitimacy of the sender using the private key of the public key cryptography algorithm. The receiver should be able to validate the legitimacy of the sender using the public key of the public key cryptography algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Branch & Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints 1

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20th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2023

作者： Hu, Xinyi Lee, Jasper C. H. Lee, Jimmy H. M. Department of Computer Science and Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Computer Sciences & Institute for Foundations of Data Science University of Wisconsin-Madison MadisonWI United States

ISBN: (数字)9783031332715

ISBN: (纸本)9783031332708

Combining machine learning and constrained optimization, Predict+ Optimize tackles optimization problems containing parameters that are unknown at the time of solving. Prior works focus on cases with unknowns only in the objectives. A new framework was recently proposed to cater for unknowns also in constraints by introducing a loss function, called Post-hoc Regret, that takes into account the cost of correcting an unsatisfiable prediction. Since Post-hoc Regret is non-differentiable, the previous work computes only its approximation. While the notion of Post-hoc Regret is general, its specific implementation is applicable to only packing and covering linear programming problems. In this paper, we first show how to compute Post-hoc Regret exactly for any optimization problem solvable by a recursive algorithm satisfying simple conditions. Experimentation demonstrates substantial improvement in the quality of solutions as compared to the earlier approximation approach. Furthermore, we show experimentally the empirical behavior of different combinations of correction and penalty functions used in the Post-hoc Regret of the same benchmarks. Results provide insights for defining the appropriate Post-hoc Regret in different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Constrained optimization

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