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Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

Annals of Data science 2024年第5期11卷 1563-1580页

作者： Kanapala, Ambedkar Pal, Sukomal Dara, Suresh Jannu, Srikanth Department of Computer Science and Engineering Geethanjali College of Engineering and Technology TS Hyderabad501301 India Varanasi India School of Technology Woxsen University TS Hyderabad502345 India Department of Computer Science and Engineering Vaagdevi Engineering College TS Warangal506005 India

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Information retrieval

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A conceptually simple and generic construction of plaintext checkable encryption in the standard model

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Designs, Codes, and Cryptography 2024年第6期92卷 1729-1746页

作者： Chen, Yu-Chi Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

Plaintext-checkable encryption (PCE) can support searches over ciphertext by directly using plaintext. The functionality of a search is modeled by a specific check algorithm that takes a pair of target plaintext and ciphertext as input and returns 1 if the correct decryption result of the ciphertext is identical to the target plaintext. A trivial solution is to use an existing scheme (e.g., deterministic RSA) to achieve this, but there is no security guarantee with this method. Previous rigorous works have either relied on some mathematical structures to build PCE that can proven in the standard model or can be generic, as in the random oracle model. Hence, in this work, we aim to construct PCE that can be proven in the standard model by using standard primitives in a modular way in two steps. The first step is to present a warm-up construction of PCE from hash garbling and hash functions whose security is only proven in the random oracle model. The second step is to provide a full-fledged construction based on the warm-up, with slight modifications for achieving security in the standard model. Finally, we show the feasibility of the proposed construction through experiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Hash functions

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PCA-IFNNR: Principal component analysis based image fusion directed nearest neighbor representation for dark face image super resolution

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Multimedia Tools and Applications 2024年 1-19页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science & amp Engineering National Institute of Technology Patna Patna800005 India

Facial image captured under uncontrolled environment often degraded by blur, noise and low-light effect which significantly affect the performance of different real life applications. To solve this problem numerous face image super resolution (FISR) methods developed in last two decades. However, getting state-of-the-art accuracy for low-light face images is still a challenging task. Hence, to solve this problem a new FISR method for dark and low-resolution face images is proposed in this paper to enhance the robustness of the existing FISR techniques against darkness (low-light) problems. The proposed method tends to remove the darkness effects present in the input low-resolution (LR) face image. This method first uses the brightness enhancement approach to generate two most suitable brighter images with different brightness levels. Further, the Principal Component Analysis (PCA) based image fusion technique is developed to fuse those two images in order to generate darkness free image. After this, min-max based contrast enhancement technique is modified and applied on the fused image to make it compatible with dataset (dictionary) images for the reconstruction purpose. Further, this enhanced image is used in nearest neighbor representation to generate its equivalent brighter and high-resolution (HR) version. The simulation results using publicly accessible data sets and some real-world images imply that the proposed method outperforms existing state-of-the-art methods in darker scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Increasing the Maximum Capacity Path in a Network and Its Application for Improving the Connection Between Two Routers

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Tsinghua science and technology 2024年第3期29卷 753-765页

作者： Adrian M.Deaconu Javad Tayyebi Department of Mathematics and Computer Science Transilvania University of BrasovBrasov 500036Romania Department of Industrial Engineering Birjand University of TechnologyBirjand 000097Iran

Abstract:This paper addresses the problem of improving the optimal value of the Maximum Capacity Path(MCP)through expansion in a flexible network,and minimizing the involved *** only condition applied to the cost functions is to be non-decreasing *** is a non-restrictive condition,reflecting the reality in practice,and is considered for the first time in the ***,the total cost of expansion is a combination of max-type cost(e.g.,for supervision)and sum-type cost(*** building infrastructures,price of materials,price of labor,etc.).For this purpose,two types of strategies are combined:(l)increasing the capacity of the existing arcs,and(l)adding potential new *** different problems are introduced and *** the problems have immediate applications in Internet routing *** first one is to extend the network,so that the capacity of an McP in the modified network becomes equal to a prescribed value,therefore the cost of modifications is minimized.A strongly polynomial-time algorithm is deduced to solve this *** second problem is a network expansion under a budget constraint,so that the capacity of an McP is maximized.A weakly polynomial-time algorithm is presented to deal with *** the special case when all the costs are linear,a Meggido's parametric search technique is used to develop an algorithm for solving the problem in strongly polynomial *** new approach has a time complexity of O(n^(4)),which is better than the time complexity of O(n4 log(n)of the previously known method from literature.

关键词： Maximum Capacity Path(MCP) network expansion Internet routing polynomial-time algorithms

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Person re-identification using vision transformer and centroid triplet loss

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Multimedia Tools and Applications 2024年第29期83卷 73777-73788页

作者： Ijjina, Earnest Paul Medipelly, Rampavan Beerukuri, Santosh Kumar Vinnakota, Sowmya Nelakurthi, Vijay Chowdary Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Warangal506004 India

In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage from a different camera or the same camera at various points in time. The goal is to accurately identify a person despite variations in their appearance due to changes in pose, illumination, and occlusions. Person re-ID is a key component in video surveillance with practical applications in public safety, retail, and transportation, among others. However, it remains a difficult problem due to the inherent variability in appearance and the lack of robust features to capture the subtle differences between individuals. Even the existing Convolution Neural Networks (CNNs) based approaches for person re-ID task struggle to address the issues due to variations in pose, occlusions, and background clutter. To tackle these issues in person re-ID task, we propose an approach using Vision Transformers (ViT) with Centroid Triplet Loss (CTL). Experimental studies conducted on Market1501 and DukeMTMC datasets, yielded better results than the existing approaches, indicating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cameras

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data

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Multimedia Tools and Applications 2024年第37期83卷 85139-85168页

作者： Dubey, Madhuri Tembhurne, Jitendra Makhijani, Richa Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Ladder-Type Resonant Switched-Capacitor Converter Achieving 99.1% Peak Efficiency and 4750 w/in3Power Density for High-Current Application

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IEEE Transactions on Power Electronics 2024年第7期39卷 8040-8053页

作者： You, Dong-Ying Chiu, Huang-Jen Kuo, Shih-Hao National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan

A nonisolated fixed-ratio resonant switched-capacitor converter (RSCC) with high peak efficiency and high power density is encouraged as intermediate bus converter. With higher input voltages, stacked topologies achieve better component stress, but the relative current stress will be multiplied, e.g., ladder and Cockcroft-Walton topologies are not well suited for high-power high step-down ratio applications. Accordingly, in this article, a ladder-type RSCC is proposed to meet high current demands by combining symmetrical auxiliary resonant tanks with two interleaved phases. In addition to having a high-resolution scalability that satisfies any integer conversion ratio, the voltage stress on the flying capacitors does not increase significantly as the conversion ratio increases, making their capacitance minimally susceptible to component tolerances. Meanwhile, all mosfets are always well clamped at 1× output low-voltage level, which means that low-voltage devices are permitted to be used, resulting in a good factor of merit. By combining adaptive constant on-time with dead-time modulation, an effective duty cycle is optimized, and controlled current sharing is achieved, making multistage parallel expansion very flexible and efficient. To demonstrate the feasibility and practicality, a compact 3-to-1 hardware prototype was realized with a load current of up to 75 A, resulting in an excellent power density of 4750 W/in3. A peak efficiency of 99.1% and a full-load efficiency of 98.1% were also achieved. © 1986-2012 IEEE.

关键词： Topology

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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