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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Syed, Liyakathunisa Department of Computer Science College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

Crop yield is a serious concern for farmers due to irregular irrigation, soil erosion, uncontrolled seed planting, severe weather conditions, peasants, and unpredicted locust infestations. The lack of current data and the intricacy of traditional agriculture leads to inefficiency and excessive operation costs. Smart farming with Artificial Intelligence techniques and the Internet of Things (IoT) in agriculture overcome these challenges by connecting all the accessible data sources into a single, fully effective functional unit. Smart agriculture helps in the sustainability of food production by using minimum resources such as water, fertilizer, and seeds. It provides a better understanding of the soil, crop, and changing weather. Furthermore, the sensors in the system assist in monitoring and controlling the resources. In this research, we present ensemble-based machine learning approaches in the IoT environment to predict crop yields and enable sustainable farming by guiding the farmers to grow the correct crop during the correct season to increase their yields. The proposed method uses a novel ensemble-based machine learning classification approach with two levels of predictions-level-0 prediction includes (Logistic Regression (LR), Classification and Regression Trees (CART), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)) classifiers are input as features to the level-1 meta classifier (Random Forest) to detect the distinct categories of different crops. Its predictions can be extremely close to the ground truth, which accurately predicts 22 categories of crops with 99% accuracy. Hence, the proposed Smart agriculture system can assist farmers in improved yield production and remote monitoring at low cost. © 2024 Elsevier B.V.. All rights reserved.

关键词： Crops

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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Multimedia Tools and Applications 2025年第14期84卷 14047-14070页

作者： Vedavathi, N. Suhas Bharadwaj, R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru570002 India Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru570008 India

Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no effective approach for the early detection of pest populations and for classifying crops as infested or non-infested. Therefore, the identification and categorization of early-stage grain infestations is in high demand. To address the above problem, this research presents a better technique for detecting and classifying infested and non-infested grains. Initially, the maize, wheat and olive image datasets are collected, which are then pre-processed using data normalization and adaptive wiener filtering (AdwF) approach to improve the excellence of the input images. Image quality helps in achieving better performance and also helps in analyzing the quality of food grains effectively. Feature extraction is done using the Dense Residual Pyramidal Dilated Convolution Network (DR-PDCNet) technique, and feature selection is done using the Linear Discriminant Coati Optimization (LD-CoA) algorithm. The Hybrid Convolutional Bi-LSTM based Deep Maxout Network (HCBiL-DMN) model is used to detect the infected food grains in the preserved food grains with high accuracy. This research explores the potential and future possibilities for using technology to ensure the quality, assessment, and monitoring of nutritional grains during storage. The performance results are achieved with 98.3% accuracy, 99.12% recall, 98.65% precision, 98.02% MCC, and 99.32% F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Privacy Comparison for Bitcoin Light Client Implementations 6

Privacy Comparison for Bitcoin Light Client Implementations

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6th Conference on Advances in Financial Technologies, AFT 2024

作者： Kotzer, Arad Rottenstreich, Ori The Department of Computer Science Technion Haifa Israel The Department of Computer Science The Department of Electrical and Computer Engineering Technion Haifa Israel

ISBN: (纸本)9783959773454

Light clients implement a simple solution for Bitcoin’s scalability problem, as they do not store the entire blockchain but only the state of particular addresses of interest. To be able to keep track of the updated state of their addresses, light clients rely on full nodes to provide them with the required information. To do so, they must reveal information about the addresses they are interested in. This paper studies the two most common light client implementations, SPV and Neutrino with regards to their privacy. We define privacy metrics for comparing the privacy of the different implementations. We evaluate and compare the privacy of the implementations over time on real Bitcoin data and discuss the inherent privacy-communication tradeoff. In addition, we propose general techniques to enhance light client privacy in the existing implementations. Finally, we propose a new SPV-based light client model, the aggregation model, evaluate it, and show it can achieve enhanced privacy than in the existing light client implementations. © Arad Kotzer and Ori Rottenstreich.

关键词： Differential privacy

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An Optimal Feature Selection-Based Approach to Predict Cervical Cancer Using Machine Learning 3rd

An Optimal Feature Selection-Based Approach to Predict Cervi...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Al Mamun, Abdullah Uddin, Khandaker Mohammad Mohi Chakrabarti, Anamika Nur-A-Alam, Md. Mahbubur Rahman, Md. Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Cervical cancer has been one of the leading causes of female early death in recent years. In developing countries, cervical cancer occurs in more than 85% of cases. Cervical cancer has been associated with several risk factors. For forecasting the prognosis of cervical cancer patients, we created a prediction model in this study based on early screening and risk trends in individual health records. In this paper, we analyze the risk variables for cervical cancer using ML classification methods. The study’s dataset is very unbalanced and includes missing values. Consequently, the ROS approach, a sampling method was used. The effectiveness of class imbalance was demonstrated by comparing the suggested model’s accuracy, sensitivity, and specificity. The RF, GB, and MLP machine learning models perform better with 99.07, 98.57, and 98.51% accuracy when using the Random Oversampling technique, and XGBoost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Forecasting Maternal Women's Health Risks using Random Forest Classifier 7

Forecasting Maternal Women's Health Risks using Random Fores...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thakkar, Dhruvi Gandhi, Vaibhav C. Trivedi, Dhriti Faculty of Engineering & Technology Computer Science & Engineering Department Vadodara India Computer Engineering Department Anand India Computer Engineering Department Vadodara India

ISBN: (纸本)9798350359299

Nowadays, maternal health during pregnancy is a major concern, especially in rural areas where risks are increased by a lack of medical experts and poor infrastructure. The lack of effective methods for predicting maternal health risks poses a significant challenge in maternal healthcare. Traditional approaches often fall short of accurately identifying and managing complications during pregnancy, leading to adverse outcomes. To overcome, this new technique can accurately assess medical data in order to forecast threats to maternal health. This research investigates machine learning algorithms' potential in predicting such risks using medical data. The study utilizes a carefully curated dataset called the Maternal Health Risk Dataset, encompassing parameters like age, blood pressure, glucose levels, body temperature, heart rate, and risk level. Through ensemble learning-based feature engineering, algorithms like Random Forest, XG Boost, Support Vector Classifier (SVC), Decision Tree, and Logistic Multiclass are assessed. Results show Random Forest achieving an impressive accuracy of 94.26%, indicating its potential in maternal health risk prediction, crucial for minimizing adverse outcomes and enabling timely interventions during pregnancy. © 2024 IEEE.

关键词： Health risks

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

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Neural Computing and Applications 2024年 1-16页

作者： Jain, Dipika Sangwan, Saurabh Raj Kumar, Akshi Department of Computer Science and Engineering Delhi Technological University New Delhi India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computing Goldsmiths University of London London United Kingdom

In the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity. © The Author(s) 2024.

关键词： Deep learning

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CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images using Deep learning by integrating Photometric Data Augmentation 9

CBIR framework for Diagnosing COVID 19 in Chest X-Ray Images...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Martey, Ezekiel Mensah Appiah, Obed Sitti, Mensah Nusetor, Foster Delali Department of Computer Science and Engineering Tarkwa Ghana Department of Computer Science and Informatics Sunyani Ghana

ISBN: (纸本)9798350385403

computer-Aided Diagnostic (CAD) systems enhance clinical decision-making, providing more accurate and streamlined processes. CAD systems based on Content-Based Image Retrieval (CBIR) serve as visual tools that strengthen the reliability of clinical decisions. While deep learning-based CAD systems have shown significant progress in extracting insights from X-rays and medical images, they often struggle due to their reliance on large datasets and high computational demands. These challenges are particularly acute in the medical domain, where datasets are frequently limited and imbalanced. This paper presents a framework for diagnosing COVID-19 from X-ray images, combining deep learning with photometric data augmentation. The proposed approach focuses on two core tasks: image retrieval and classification, which depend on robust feature representation. Using Euclidean distance measures, we utilise a modified EfficientNet-B3 model for feature extraction and examine the effects of transfer learning and fine-tuning on retrieval and diagnostic accuracy. Evaluating binary and multiclass datasets reveals that the proposed method performs strongly in classification tasks, with notable effectiveness in binary and multiclass scenarios. Compared to state-of-the-art techniques, our approach demonstrates superior accuracy for binary classification of X-ray images, outperforming most existing models. © 2024 IEEE.

关键词： COVID-19

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Elevating Image Steganography:A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

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computers, Materials & Continua 2024年第5期79卷 2923-2943页

作者： Muhammad Zaman Ali Omer Riaz Hafiz Muhammad Hasnain Waqas Sharif Tenvir Ali Gyu Sang Choi Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongbuk38541Korea

In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

关键词： Steganography most significant bit(MSB) least significant bit(LSB) peak signal-to-noise ratio(PSNR)

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Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and Aggregation

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IEEE Transactions on Multimedia 2024年 26卷 7775-7786页

作者： Umam, Ardian Yang, Cheng-Kun Chuang, Jen-Hui Lin, Yen-Yu National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan

We propose a co-part segmentation method that takes a set of point clouds of the same category as input where neither a ground truth label nor a prior network is required. With difficulties caused by the label absence, we formulate the co-part segmentation task into two subtasks, including superpoint generation and part aggregation. In the first subtask, our superpoint generation network divides each point cloud into homogeneous partitions, each called superpoint, while in the second subtask, these superpoints are further aggregated into a few semantic parts via our part aggregation network. We introduce the coupled attention blocks in the part aggregation network to explicitly enforce semantic consistency in the segmentation by exploiting intra-, inter-, and paired-cloud geometrical information by minimizing the devised intra-, inter-, and paired-cloud losses, respectively. The intra-cloud loss triggers a semantic segmentation in each point cloud, while the inter-cloud loss considers all clouds to enforce their semantic consistency. The paired-cloud loss is designed to ensure that each part of one point cloud can be discriminatively reconstructed from the superpoints of another point cloud. We perform experiments on two benchmark datasets, ShapeNet part and COSEG, and provide quantitative and qualitative results to demonstrate the superiority of our method over existing methods. We also show that the proposed method can help several downstream tasks, including semi-supervised part segmentation and data augmentation for shape classification. © 1999-2012 IEEE.

关键词： Semantics

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