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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： De, Minati Khurana, Sambhav Singh, Satyam Department of Mathematics Indian Institute of Technology Delhi New Delhi India Department of Computer Science and Engineering Texas A &M University College StationTX United States

ISBN: (纸本)9783031496103

In this paper, we present online deterministic algorithms for minimum coloring, minimum dominating set and its variants in the context of geometric intersection graphs. We consider a graph parameter: the independent kissing number ζ, which is a number equal to ‘the size of the largest induced star in the graph - 1 ’. For a graph with an independent kissing number at most ζ, we obtain an algorithm having an optimal competitive ratio of ζ, for the minimum dominating set and the minimum independent dominating set problems;however, for the minimum connected dominating set problem, we obtain a competitive ratio of at most 2 ζ. In addition, we prove that for the minimum connected dominating set problem, any deterministic online algorithm has a competitive ratio of at least 2 (ζ- 1 ) for the geometric intersection graph of translates of a convex object in R2. Next, for the minimum coloring problem, we present an algorithm having a competitive ratio of O(ζ′log m) for geometric intersection graphs of bounded scaled α -fat objects in Rd having a width in between [1, m], where ζ′ is the independent kissing number of the geometric intersection graph of bounded scaled α -fat objects having a width in between [1, 2]. Finally, we investigate the value of ζ for geometric intersection graphs of various families of geometric objects. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coloring

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Path Texture Detection for Visually Impaired using Transfer Learning 3

Path Texture Detection for Visually Impaired using Transfer ...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Manjari, Kanak Animesh Verma, Madhushi Singal, Gaurav Bennett University Computer Science Engineering Gr. Noida India National Institute of Technology Computer Science & Engineering Delhi India Netaji Subhas University of Technology Computer Science Engineering India

ISBN: (纸本)9798350300710

The computer vision technique known as object detection is used to determine the location of an object in an image or video. Recent advances in a number of areas, including computer vision, speech recognition, and natural language processing have been made using deep learning approaches. Convolution neural networks (CNNs), which have several layers, are used to detect objects. A deep learning model that uses CNN to identify various surface regions has been suggested. The study of tracks, walkways, playgrounds, highways, and muddy areas is a pertinent and significant aspect of object detection for Visually Impaired (VI) individuals. In addition to adding indirect vision to the blind individual, this model recognizes surface regions and alerts them using text-to-sound conversion API. In this study, transfer learning was carried out utilizing Faster RCNN (Region Based Convolutional Neural Networks) with Inception V2 and ResNet50 model weights. The accuracy of both trained models was the same, while Faster RCNN Inception V2 had the lowest loss (0.02). © 2023 IEEE.

关键词： Object detection

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AR-GeoShapes: Development and Evaluation of a Marker Less Augmented Reality Based Geometrical Shapes Learning Framework on Memory Retention and Learning Achievement 18

AR-GeoShapes: Development and Evaluation of a Marker Less Au...

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

作者： Azam, Ayesha Naz, Zubaira Khan, Muhammad Usman Ghani Din, Irfanud Al-Khwarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Information Technology University Lahore Pakistan University of Engineering and Technology Department of Computer Science Lahore Pakistan New Uzbekistan University Department of Computer Science School of Computing Tashkent Uzbekistan

ISBN: (纸本)9798331508692

A large number of students struggle to grasp geometric principles, especially in three-dimensional (3D) space. In turn, this is why digital educational tools have been integrated more and more into the learning environment: because they entertain students. One of these technologies is Augmented Reality (AR) which has become a widely researched and effective tool for education. Where learning is situated with content embedded within the physical environment (support), AR better enables offerings to be adapted. This paper presents AR-GeoShapes, an augmented reality-based educational tool developed to help students learn solid geometry concepts. Built with the Vuforia marker less SDK in Unity, AR-GeoShapes is an exercise that runs on mobile devices for users to learn shapes interactively. Further to evaluate whether using AR-GeoShapes helps increase memory retention and improve learning achievement, a comparative study involving 58 students was conducted. The results demonstrate that the experimental group, utilizing AR-GeoShapes, exhibited significantly higher memory retention and learning outcomes compared to the control group. These findings suggest that AR-GeoShapes are a valuable tool for enhancing students' comprehension of geometric concepts. © 2024 IEEE.

关键词： Contrastive Learning

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Finding Friendly Neighborhood: Optimal D2D Relaying in mmWave IoT Networks

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IEEE Access 2025年 13卷 89193-89207页

作者： Krishna, J. Vamshi Vijay Mahendran, V. Badarla, Venkataramana Indian Institute of Technology at Tirupati Department of Computer Science and Engineering Andhra Pradesh Tirupati517619 India

Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-Things (IoT) network. The key factors involve the placement of relays and neighbor selection (NS) (i.e., the process of selecting source IoTs within the relay node's coverage range). The problem is formulated using model-based bandits, and the associated structural results are analyzed. Considering the resource-constrained nature of IoTs, we have developed a simple rule-based NS as an alternative to computationally intensive learning-based neighbor-selection processes from state of the art. By utilizing the rule-based NS, we show that there exists a conducive coverage range (called a friendly neighborhood) for the relay node to choose the neighbors. Accordingly, a geometry-based relay placement mechanism is proposed to maximize the uplink throughput. The optimality of the proposed solutions is shown analytically, and their performance concerning the state of the art is evaluated using extensive simulation. © 2013 IEEE.

关键词： Beamforming

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A Review on Biodegradation of Plastics with Organic Fraction of Municipal Solid Wastes by Anaerobic Co-digestion

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Water, Air, and Soil Pollution 2025年第3期236卷 1-41页

作者： Praveen, Penaganti Chatterjee, Biswabandhu Mazumder, Debabrata Department of Civil Engineering Indian Institute of Engineering Science and Technology Shibpur West Bengal Howrah711103 India Department of Civil and Environmental Engineering C. V. Raman Global University Mahura Odisha Bhubaneswar752054 India

Improper plastic disposal results in biological magnification since the plastics accumulated in the landfills and in the ocean find their way into the food web – getting increasingly accumulated at the top of the Ecological Pyramid. The degradation of plastic waste can be achieved by chemical, thermal, photo, and biological processes. Anaerobic co-digestion (AcoD) can be applied to biodegrade plastic waste and the organic fraction of municipal solid waste (OFMSW) while ensuring increased process stability and biogas production. While the anaerobic digestion (AD) or anaerobic mono-digestion of only plastics with high carbon content (higher carbon-to-nitrogen ratio) is challenging, the co-digestion with lower carbon-to-nitrogen ratio organic wastes results in increased biodegradation and biogas production. Pretreatment of plastic waste can surely enhance biodegradability and biogas yield, but further investigation is required to determine the economic viability of various pretreatment techniques available. This review highlights the classification of plastics based on their biodegradability, microbial species responsible for biodegradation, and the changes in the properties of plastics during biodegradation under AD and AcoD. Further, the review delves into the crucial process governing factors that affect AcoD and provides current insights for plastic biodegradation using AcoD. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Anaerobic digestion

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Maximum Available Power Estimation in Solar Photovoltaic Power Plants Using Reference Inverters: A Critical Assessment

Maximum Available Power Estimation in Solar Photovoltaic Pow...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Maity, Soudipan Rather, Zakir Hussain Doolla, Suryanarayana Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798350396782

With the ongoing displacement of conventional generators in the power systems by renewable energy (RE) sources, the grid operators are increasingly mandating strict grid code requirements on the RE plants to regulate their operation to ensure the frequency and voltage stability of the grid. Estimating the instantaneous maximum available power (MAP) in large-scale RE plants is paramount to evaluating their capability to retain active power reserves and participate in frequency regulation. Assessing the MAP for RE plants is challenging due to the intermittency and variability of the resources. For solar photovoltaic power plants (PVPPs), external factors such as changing cloud cover, air temperature and soiling make it more demanding to accurately estimate the MAP, primarily in deloaded operating mode. In this context, the operation of the PV inverters in a master/slave configuration has often been utilized as a potential method for MAP estimation. This paper discusses one such innovative technique for MAP estimation and assesses its effectiveness in the field. A methodology is proposed to determine the optimal number of master (reference) inverters and their estimation interval depending on the plant layout and the forecasted cloud motion and help evaluate the PVPP’s frequency regulation capability with minimum computational complexity. © 2023 IEEE.

关键词： cloud estimation photovoltaic reserve shading

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A Well Structured Design for Individualized Healthcare System via Fuzzy Logic System (FLS) 4

A Well Structured Design for Individualized Healthcare Syste...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Ltd India Pune India Faculty of Management It & Business Analytics India Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

Personalized medicine is driving the need for new and customized biopharmaceuticals using proteins that have been changed. As such, it is critical to evaluate these items for allergenicity before introducing them as treatments. Even though there are a number of techniques available to predict the allergenicity of proteins, it may still be difficult to discriminate between allergens and nonallergens, particularly when the allergens have a considerable sequence resemblance to their known counterparts. To address this, we provide 'FuzzyApp,' a novel fuzzy rule-based system that evaluates a query protein's potential for allergenicity. FuzzyApp uses fuzzy IF-THEN rules that come from five different modules to determine how allergenic a protein is. FuzzyApp outperforms the state-of-the-art techniques on a variety of datasets, attaining a pleasing trade-off between specificity and sensitivity, as shown by an upbeat Mathew's correlate coefficient. FuzzyApp's capacity to distinguish allergens from allergen-like potential nonallergens (APN) is highlighted by its notable high specificity in this regard. The efficacy and uniformity of this suggested approach are further supported by error analysis and analysis of the whole proteome dataset. Furthermore, Tropomyosin from a variety of allergic and nonallergenic sources is properly predicted by FuzzyApp. The companion online service helps users comprehend the reasoning behind predictions by facilitating batch sequence filing and presenting findings in coherent words. © 2024 IEEE.

关键词： Personalized medicine

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Instantaneous Fundamental Frequency Estimation based on FBSE-FAWT 9

Instantaneous Fundamental Frequency Estimation based on FBSE...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Vijay, Amishi Pachori, Ram Bilas Appina, Balasubramanyam Tiwari, Nitya Department of Electrical Engineering Indian Institute of Technology Indore Indore India School of Electrical Science Indian Institute of Technology Bhubaneshwar Bhubaneshwar India

ISBN: (纸本)9798350383201

Instantaneous fundamental frequency (IFF) estimation plays an important role in speech processing. This paper proposes a method for estimating a speech signal's IFF based on the Fourier-Bessel series expansion-based flexible analytic wavelet transform (FBSE-FAWT). The proposed method uses an FBSE-FAWT-based filter bank to decompose the signals into subbands. Components corresponding to the fundamental frequency (FF) can be present in more than one subband. Therefore, a set of subbands are selected whose mean frequency is found to be in the range of 50-500 Hz. In each subband, there may be components separated in the time domain. So for the separation of these components, a new method for time-domain segmentation (TDS) using the Teager-Kaiser energy operator (TKEO) is introduced. Then Hilbert transform separation algorithm (HTSA) based processing is performed on these separated components to obtain the IFF of the speech signal. As a post-processing step, cubic spline interpolation is performed to get accurate IFF. To validate the proposed method, the Keele database is used and the results are compared with the traditional methods. Experimentally it is proved that the IFF estimation method based on FBSE-FAWT is working better and improves the time-resolution of IFF estimation. © 2023 IEEE.

关键词： Time domain analysis

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