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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

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Journal of Bionic engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

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ROWIA: A Smart IoT-Based Reverse Osmosis Wastewater Irrigation System for Agriculture

ROWIA: A Smart IoT-Based Reverse Osmosis Wastewater Irrigati...

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2024 International Conference on engineering Management of Communication and technology, EMCTECH 2024

作者： Panah, Ali Haghighat Taghizadeh, Seyedreza Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran

ISBN: (纸本)9798331507909

Water scarcity is a significant global challenge, worsened by global warming and population growth, straining water resources. Agriculture, a major water user, often employs inefficient irrigation methods, leading to excessive water consumption. A smart irrigation system using IoT technology and Reverse Osmosis (RO) wastewater from water purifiers can help conserve water. This paper presents a smart solution leveraging the Internet of Things (IoT) technology to recycle RO wastewater for agricultural use, conserving potable water resources. The system is powered by the ESP32 microcontroller and DHT22 sensors for getting vital data such as soil moisture level, tank level, temperature, and humidity from the environment, to determine the proper irrigation timing for plants. We also use the Blynk cloud platform, enabling farmers to monitor their farms in real-time. The proposed system not only ensures appropriate irrigation timing but also promotes efficient RO wastewater usage, contributing to water conservation. © 2024 IEEE.

关键词： Soil moisture

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Statistical Detection of Malicious iOS Apps through Ensemble and Active Learning 2023

Statistical Detection of Malicious iOS Apps through Ensemble...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Bhatt, Arpita Jadhav Agarwal, Parul Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology India

ISBN: (纸本)9798400700224

iOS is one of the most broadly used mobile operating systems after Android. In today's era, smartphones are widely used to perform several tasks such as net banking, GPS tracking, ordering products, etc. One of the main features of smartphones is that their capabilities can be expanded by installing third-party apps. However, these apps pose privacy threats to the user as they contain a lot of user's personal information, which may be vulnerable to attackers. Background studies have identified that a lot of user's personal and sensitive data is stored on smartphones and thus, they have become a perfect target for privacy threats in the whole mobile ecosystem. Machine learning techniques to detect malware apps have offered promising results for the Android platform whereas nominal work has been done for the iOS platform. In this paper, we are incorporating various machine learning techniques to detect malicious iOS apps from several categories. Some of the ensemble learning techniques with different classification algorithms are inculcated to enhance the precision of classifiers. A weighted average method on active and ensemble learning has been proposed which proved to be more efficient as compared to other machine learning techniques in most of the categories. Our empirical analysis has demonstrated that the proposed technique is statistically significant than active and ensemble learning. © 2023 ACM.

关键词： Smartphones

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Ramp Progressive Secret Image Sharing using Ensemble of Simple Methods 14

Ramp Progressive Secret Image Sharing using Ensemble of Simp...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Mokhtari, Atieh Taheri, Mohammad Department of Computer Science and Engineering and Information Technology Shiraz University Shiraz Iran

ISBN: (纸本)9798331511272

This paper reviews the limitations of traditional Secret Image Sharing (SIS) methods and recent advancements such as PSIS, RPSIS, and SSIS, and presents an optimized and practical combinatorial method for secret image sharing by ensemble of simple methods. In this approach, a color or grayscale image is divided into n shares, each of which is individually meaningless. The image is reconstructed through XOR operations between corresponding pixels in different shares. The main innovation of this method is the introduction of two thresholds, k1 and k2. This scheme is characterized by parameters (k1, k2, n), where having fewer than k1 shares reveals no information about the original image, while between k1 and k2 shares, the image quality progressively improves and reaches near-complete reconstruction. Ultimately, with k2 or more shares, the image can be recovered in its full resolution. This method allows for customization of the image quality improvement rate between k1 and k2 and addresses the limitations of previous designs regarding the selection of k1, k2, and n values. Its flexibility in providing suitable models based on specific user needs makes this method appropriate for various applications in secret image sharing. ©2024 IEEE.

关键词： Progressive Recovery Progressive Secret Image Sharing Ramp Secret Image Sharing Secret Image Sharing Secret Sharing Visual Cryptography

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Ethical Dimensions in Data-Driven Business: Balancing Fairness, Accountability, Transparency, and Explainability 15

Ethical Dimensions in Data-Driven Business: Balancing Fairne...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

In the modern business landscape, the widespread adoption of data-driven decision systems underscores the importance of integrating ethical principles such as fairness, accountability, transparency, and explainability (FATE). These principles are critical for ensuring that decision-making processes are conducted ethically and responsibly. Fairness prevents biases and discrimination, accountability ensures organizations are held responsible for outcomes, transparency fosters trust by making processes accessible, and explainability enables users to comprehend algorithmic decisions. This paper examines the significance of FATE principles in business contexts, emphasizing their role in promoting ethical decision-making and societal welfare. Through case studies and best practices, it highlights the practical implementation of these principles to ensure the responsible use of data-driven technologies in business operations. © 2024 IEEE.

关键词： Data assimilation

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A top-down character segmentation approach for Assamese and Telugu handwritten documents

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3275-3287页

作者： Dutta, Prarthana Muppalaneni, Naresh Babu Department of Computer Science and Engineering National Institute of Technology Assam Silchar786003 India Department of Computer Science and Engineering The Assam Kaziranga University Assam Jorhat785006 India Indian Institute of Information Technology Design and Manufacturing Kurnool Andhra Pradesh Kurnool India

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object detection

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Dimensionality Reduction and Gradient Boosting for In-Vivo Hyperspectral Brain Image Classification

Dimensionality Reduction and Gradient Boosting for In-Vivo H...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Singh, Raj Bahadur Datta, Aloke The Lnm Institute of Information Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350376876

Hyperspectral imaging (HSI) datasets contain hundreds of contiguous narrow spectral bands, which can create challenges in data analysis due to the curse of dimensionality. However, much of this data is redundant, necessitating the application of dimensionality reduction techniques to extract the most relevant features before the classification task. Two feature extraction methods, principal component analysis (PCA) and non-negative matrix factorization (NMF), are explored. Then, the reduced feature sets are used to train three gradient-boosting algorithms, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), for the classification of in-vivo hyperspectral brain images. The experimental results show the effectiveness of gradient boosting over support vector machine (SVM) and random forest (RF) in performance measures like accuracy, sensitivity, specificity, and F1-score. © 2024 IEEE.

关键词： Non-negative matrix factorization

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Combined Architecture of Destination Sequence Distance Vector (DSDV) Routing with Software Defined Networking (SDN) and Blockchain in Cyber-Physical Systems

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computers, Materials & Continua 2025年第2期82卷 2311-2330页

作者： Jawad Ahmad Ansari Mohamad Khairi Ishak Khalid Ammar School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong TebalPulau Pinang11800Malaysia Department of Electrical and Computer Engineering College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates

Cyber-Physical System (CPS) devices are increasing exponentially. Lacking confidentiality creates a vulnerable network. Thus, demanding the overall system with the latest and robust solutions for the defence mechanisms with low computation cost, increased integrity, and surveillance. The proposal of a mechanism that utilizes the features of authenticity measures using the Destination Sequence Distance Vector (DSDV) routing protocol which applies to the multi-WSN (Wireless Sensor Network) of IoT devices in CPS which is developed for the Device-to-Device (D2D) authentication developed from the local-chain and public chain respectively combined with the Software Defined Networking (SDN) control and monitoring system using switches and controllers that will route the packets through the network, identify any false nodes, take preventive measures against them and preventing them for any future problems. Next, the system is powered by Blockchain cryptographic features by utilizing the TrustChain features to create a private, secure, and temper-free ledger of the transactions performed inside the network. Results are achieved in the legitimate devices connecting to the network, transferring their packets to their destination under supervision, reporting whenever a false node is causing hurdles, and recording the transactions for temper-proof records. Evaluation results based on 1000+ transactions illustrate that the proposed mechanism not only outshines most aspects of Cyber-Physical systems but also consumes less computation power with a low latency of 0.1 seconds only.

关键词： DSDV intelligent authentication SDN control&monitoring blockchain recording of transactions

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Grouped Guidance Convolutional Networks for Absolute 3D Human Pose and Joint Rotations Estimation 7

Grouped Guidance Convolutional Networks for Absolute 3D Huma...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Zhang, Hua Li, Shujie Jia, Wei Hefei University Of Technology Department Of Computer Science And Information Engineering Hefei China

ISBN: (纸本)9798350361445

In the current landscape of monocular single-person 3D human pose estimation, the primary focus lies in recovering the 3D coordinates of human joints relative to the root joint from monocular images or videos. However, there has been a disregard for the estimation of absolute 3D pose and joint rotations, limiting the application of human pose estimation in fields such as virtual reality and computer animation. In this paper, we address this issue by proposing a Grouped Guidance Convolutional Network capable of estimating 3D absolute pose and joint rotations concurrently. The input to the network consists of 2D joint coordinates, preprocessed using camera parameters. The Grouped Guidance Network extracts shared features between 3D joint coordinates and joint rotations, and through training, the network obtain joint rotations, while a Skeleton Length Network estimates the lengths of human bones. The joint rotations and skeleton lengths are then fed into a forward kinematics layer to obtain the root-relative pose. Simultaneously, the preprocessed data, along with foot contact information outputted by the Foot Contact Network, is input into a Trajectory Network. This network extracts potential connections between foot contact information and the trajectory of the human root joint, further outputting the coordinates of the root joint trajectory. Finally, by combining the root-relative pose and the root joint trajectory, the 3D absolute pose of the human body is obtained. Quantitative and qualitative experiments demonstrate that the proposed method achieves optimal results in absolute pose estimation, surpassing other methods capable of outputting joint rotations. © 2024 IEEE.

关键词： Trajectories

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