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Assessing the impact of Covid-19 on the Italian comic book industry: a panel vector autoregressive analysis of financial ratios

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Quality and Quantity 2025年 1-29页

作者： Magrini, Alessandro Rimediotti, Luca Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence 50134 Italy Independent Researcher Prato 59100 Italy

The comic book market is a niche that has significantly benefited from the more time spent at home by people during the outbreak of Covid-19. This is particularly true in Italy, where sales of comics have met an unprecedented expansion during the two decades before the pandemic. It is therefore important for policy makers to understand the impact of Covid-19 on the economic performance of the Italian comic book industry. To this purpose, yearly data on 10 financial ratios are collected for a panel of 13 Italian comic publishers in the period 2011-2022, and an interrupted time series (ITS) design based on panel vector autoregressive (PVAR) models is applied. In order to maximize the statistical power of the design given the small number of both time points (T=9) and cross-sectional units (n=13), we correct least squares estimates based on the half panel jackknife and perform model reduction through system sequential elimination. The results highlight a worsened performance for small publishers used to base most of their business on comic fairs (Round Robin, Magic Press Edizioni), while companies able to renew their business by launching new products and re-editions (the historic Astorina and Sergio Bonelli Editore, but also the young Tunué) experienced a significant growth compared to the pre-pandemic period. Publishers that dedicated most of their activity to the publication of manga (Bao Publishing, Coconino, Edizioni BD, Edizioni Star Comics, and also Panini with the label Planet Manga) received even more benefits from the pandemic. © The Author(s) 2025.

关键词： Comics Firm performance Half panel jackknife Interrupted time series Panel VAR models

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CoroNet: Predicting Coronary Artery Disease using Deep Convolutional Neural Networks and Ensemble Model for Enhanced Diagnostic Precision 10

CoroNet: Predicting Coronary Artery Disease using Deep Convo...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Juliet, A.Hency Kalaiselvi, K. Sathya, K. Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Applications Chennai602105 India Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350353068

The coronary artery disease (CAD) is a cardiovascular ailment marked by the constriction or obstruction of the coronary arteries, hindering the circulation of blood to the heart muscle and potentially causing chest pain, heart attacks, or other complications in the cardiac system. Forecasting Coronary Artery Disease with deep learning encounters difficulties including scarce and unbalanced data due to privacy issues and the infrequency of certain conditions, alongside the imperative for transparency and comprehensibility in the decision-making of robust yet inscrutable models such as Deep Convolutional Neural Networks (CNNs). In mitigating the constraints posed by limited and imbalanced data in the prediction of CAD, sophisticated deep learning approaches, including the utilization of Ensembles of Deep CNNs and implementing Transfer Learning with Pre-trained Models, are utilized to improve the resilience and adaptability of the models. The suggested system, utilizing advanced Deep CNN techniques, provides increased predictive precision by utilizing ensemble models and transfer learning, thereby improving resilience through the use of pre-trained models for optimal feature extraction. This study indicates that the Deep CNN technique attained an 81.97% accuracy, while Ensembles of Deep CNNs showcased a marginally superior accuracy of 83.61.% in predicting coronary artery disease, emphasizing the subtle distinctions in their predictive capabilities. © 2024 IEEE.

关键词： Neural network models

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Designing an Intelligent Model that Employs Advanced Machine Learning Techniques to Optimize Disease Prediction and Classification in Humans 1

Designing an Intelligent Model that Employs Advanced Machine...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Kumari, Sudesh Lekshmi, V. Arathi Chandran, R.I. Shankar, Jaya Vishnu Priya, P.M. Bps Women University Department of Electronics and Communication Engineering Sonipat India Christ Nagar College Department of Computer Science and Applications Kerala Maranalloor India Sree Narayana Institute of Technology Department of Computer Science and Engineering Kerala Adoor India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350307795

Machine learning (ML) has emerged as a powerful tool with applications across diverse fields, including clinical diagnostics. With the growing prevalence of heart and liver diseases worldwide, early detection is paramount for saving lives. This study presents a hybrid ML model that combines the support vector machine (SVM) approach with a modified particle swarm optimization (PSO) model for the classification of heart and liver disease data. Utilizing datasets from the UCI ML repository, we evaluate the model's performance using various metrics, including categorization accuracy, error rates, correctness, recall, and the F1 score. Comparative analyses with traditional SVM and other hybrid algorithms such as the PSOSVM and Crazy PSO-SVM (CPSOSVM) are conducted. The experiments encompass the prediction of cardiac and liver illness. Results demonstrate that the designed Cauchy Crazy PSOSVM (CCPSOSVM) algorithm outperforms other approaches, achieving the greatest accuracy rate and least error rate. While this study focuses on cardiac and hepatic illness prediction, the CCPSOSVM algorithm holds promise for the extension of disease prediction across various medical domains. © 2024 IEEE.

关键词： Support vector machines

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Core Tumor Prediction and Feature Segmentation by using Weber's Law in Brain Images 8

Core Tumor Prediction and Feature Segmentation by using Webe...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Punitha, M. Kumar, M. Sathish Mangayarkarasi College of Arts and Science for Women Department of Computer Science and Applications Madurai Tamilnadu Paravai India Tamilnadu Madurai India

ISBN: (纸本)9798350376425

The presented paper proposes a feature to extract the exact core tumor portions from brain images using modified weber's law. Generally, Weber's Law is mostly used to identify the background and fore ground of an image, that knowledge encourages us to implement Weber law in MRI images for core tumor detection. Usually, tumors are in high gray level in T2-weighted and FLAIR Magnetic resonance imaging (MRI) scan images. Tumors are complex structure which contains different types of tissues called substructures. The physicians diagnose the tumor stage and surgical plan based on the substructures. Hence, the proposed work focuses to locate the substructure. Subsequently, the proposed method modifies the parameters used in Weber's law corresponding to the tumor appearance in MRI images. © 2024 IEEE.

关键词： Magnetic resonance imaging

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An IoT Cloud Security System Powered by Blockchain for Secure Preservation of Electronic Health Records

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SN computer science 2024年第7期5卷 945页

作者： Rani, K. Lino Fathima Chinna Anuradha, M.P. Department of Computer Applications Bishop Heber College Affiliated to Bharathidasan University Tamilnadu Tiruchirappalli 620017 India Department of Computer Science Bishop Heber College Affiliated to Bharathidasan University Tamilnadu Tiruchirappalli 620017 India

Common public under-monitored medical care – patients have multiple medical reports from multiple hospitals around the globe. The timely availability of medical reports from multiple sources without any tampering is crucial for patients under critical care. Blockchain Technology has been implemented to tackle the problems of the medical field – doctors, nurses, and medical professionals. A Blockchain with the Internet of Things (BIoT) is being suggested here, combining both potentials to achieve a solution to the problem statement. Research studies scrutinize the vulnerability of IoT devices to various threats and attribute them to the Lax Protocol. The criticality and sensitivity, along with the existing vulnerability, reiterates the need for better solutions. Such predispositions pave the way for attackers to swiftly destroy CIA (Confidentiality, Integrity, Availability) triad elements, the core of security. The prime goal of this work is to ensure electronic health against Distributed Denial of Service (DDoS) and probe attacks exercising recommended mechanisms like Time to Live (TTL) and DoS Protection for Distributed Ledgers (DPDL). Their focus on the limitations of IoT in protecting electronic health record records from external hackers and attacks is crucial. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attacks Blockchain DDOS IoT Security

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A Trailblazing Framework of Security Assessment for Traffic Data Management

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Intelligent Automation & Soft Computing 2023年第8期37卷 1853-1875页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Neha Yadav Syed Anas Ansar Pawan Kumar Chaurasia Alka Agrawal Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia

Connected and autonomous vehicles are seeing their dawn at this *** provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,*** vehicles generate a large amount of data,which makes privacy and security a major challenge to their *** complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their *** could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future *** the other hand,congestion is still a bottleneck for traffic management and *** research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and *** devices placed along the road are used to communicate with passing cars and collect their *** collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road ***,this data will be stored in the memory pool,where other devices will also store their *** a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic *** information is then used in two different ***,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce ***,the data stored on the blockchain will provide historical,statistical data that can facilitate the

关键词： Connected and autonomous vehicles(CAVs) traffic data management ethereum blockchain road side units smart cities

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Revolutionizing Plant Health Monitoring: A CNN-Based Solution 9

Revolutionizing Plant Health Monitoring: A CNN-Based Solutio...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Gaikwad, Rajashri Nitin Bhoite, Sachin Dr.Vishwanath Karad Mit World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798350377972

Plants are essential to country economy, agriculture, and climatic stability. However, a number of illnesses brought on by fungus, viruses, and bacteria pose a threat to plant health, thus early disease diagnosis is essential to preventing extensive damage. In this paper, a deep learning-based model for leaf image-based plant disease diagnosis is introduced. The Convolutional Neural Network (CNN) used in the suggested model was trained on the Plant Village dataset and has numerous convolution and pooling layers. The dataset is expanded by the use of data augmentation techniques, which guarantees strong model performance. After training, the model evaluated on 80% of the dataset, which contains photos of both healthy and ill plants, and it obtained a high accuracy of 86.3%. The study provides a more objective and effective method of identifying plant diseases by demonstrating the usefulness of deep learning or plant disease recognition. Future integration of the model with drones or automated systems could facilitate real-time disease detection and localization, enabling timely intervention in the agricultural field. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing Artificial Intelligence for Advance Data science and Analysis 2

Utilizing Artificial Intelligence for Advance Data Science a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Deepak Chaudhary, Gaurav Rajput, Kapil Singhal, Amit Sharma, Tarun Kumar Srm Institute of Science and Technology Department of Computer Applications UPModinagar Delhi India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Applications UP Ghaziabad India

ISBN: (纸本)9798350371055

Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever. © 2024 IEEE.

关键词： Algorithms Correlations Fundamentals Technology Understand

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RECRIoT-MAC: A Reliable and Energy Efficient Resource Management for Internet of Things Based Cognitive Radio Multi- Channel MAC Protocol

RECRIoT-MAC: A Reliable and Energy Efficient Resource Manage...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Currently, the overcrowding of unlicensed spectrum bands is causing severe interference to IoT devices embedded with sensors. CR-IoT networks apply cognitive radio capabilities to the internet of things to solve the spectrum scarcity issue in conventional IoT networks. An CR radio detects available spectrum channels by dynamically modifying its programming and configuration. IoT, smart cities, and 5G all benefit from cognitive radio applications. A cognitive radio application is useful for IoT, smart cities, and 5G. Compared to benchmark Cognitive Radio MAC (CR- MAC) protocols, the proposed protocol is more reliable, intelligent, and smart. CR technology for IoT applications can be advanced by us in order to make IoT more affordable and applicable. It is demonstrated by comparing the proposed RECRIoT-MAC protocol with benchmark CR-MAC protocols that is more energy efficient and consumes less communication time. This paper examines RECRIoT-MAC's energy consumption and throughput. © 2024 IEEE.

关键词： Cognitive radio

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Meta-optimization for improved efficiency: an integrated methodology

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2375-2386页

作者： Singha, Anjani Kumar Yadav, Gaurav Hasanpuri, Vijay Patel, Rajiv Ranjan Department of Computer Applications SRM Institute of Science and Technology (‘Deemed-to-Be-University’) Delhi-NCR Campus Ghaziabad 201204 India Department of Computer Science Aligarh Muslim University Aligarh India UIET Kurukshetra University Kurukshetra India Haridwar University Roorkee India

The use of optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizers in various problems. The objective of this study is to develop a comprehensive framework that combines meta-optimization techniques with traditional optimization methods to enhance performance metrics such as convergence speed, solution quality, and robustness against local minima. The researcher presents ATMO (Adaptive Meta Optimizers), recent deep neural network (DNN) models that are combined with a new optimizer that combines two different optimizers simultaneously. Meta-optimizer considers all of their contributions at once, rather than trying to enhance each one separately. Using a wide range of techniques, DNNs have been the subject of numerous investigations on text and image categorization. Results from these tests demonstrated that the suggested Adaptive Meta Optimizer outperforms individual optimizers. These experiments show that adequate training is achieved by considering a variety of factors such as Keras standard learning rate and convergence, and degradation on the CIFAR-100, CIFAR-10, and MNIST datasets, and the results are obtained using Python. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： AdapTiveMeta Optimizers-ATMO Deep learning Image classification Optimization algorithm Optimizers Text classification

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