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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： K. Aravinda Tejaswini Rangaraju Atul Singla Ashish Parmar Sajjad Ziara Devisingh Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq JNTUH University College of Engineering Sultanpur Telangana India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Artificial intelligence (AI) and the fourth industrial revolution (IR) are gaining increasing significance in the Anthropocene epoch - characterized by a large-scale alteration of the Earth by humans, as a geological time period which could have started as recently as several dozen years ago. The anthropogenic alteration comprises habitat loss, global warming and the growing population. This study explores how the fourth industrial revolution (IR) and AI are likely to redefine human and environmental health in the context of the ‘nascent bio-digital revolution’ (NBD) focusing on machine-human interactions, and how humans are adapting to such technological innovation speculatively in altered urban and rural landscapes 2022–2030. This paper demonstrates the impact of AI enabled innovation on the Sustainable Development Goals (SDGs) across 22 countries, utilising system dynamics modelling, and Institutional Theory. The outcome of the study illustrates that in the near term over the next eight years, there may be more pressure on SDGs such as No Poverty, Zero Hunger, Good Health and Well-Being, Quality Education, Gender Equality, Clean Water and Sanitation, Affordable and Clean Energy, Decent Work and Economic Growth, Industry, Innovation & Infrastructure, Reduced Inequalities, Sustainable Cities and Communities, etc. compared with goals like Climate Action, Life Below Water, Life on Land, Peace, Justice and Strong Institutions, and Partnerships for the Goals.

关键词： Technological innovation Costs System dynamics Navigation Urban areas Market research Smart systems Fourth Industrial Revolution Artificial intelligence Sustainable development

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data Analytics

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： H. Anwer Basha L. Ramalingam MD Ashfaqul Hasan M.H Ananda S. Velmurugan S.P. Maniraj Department of Computer Science Saveetha College of Liberal Arts & Sciences SIMATS University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India School of Electrical and Electronics Engineering REVA University Bangalore Karnataka India Department of Electronics and Communication Engineering TJS Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions.

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A Comprehensive Analysis of the Use of Machine Learning Techniques to Optimize the Information Retrieval Process

A Comprehensive Analysis of the Use of Machine Learning Tech...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： S. Hemelatha Kalanandhini. G Prasad B. Chaudhari Rajeev Sharma Jambi Ratna Raja Kumar Dipannita Mondal Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon

This explores using a system to gain knowledge of strategies that allow you to optimize the facts retrieval manner. Facts retrieval is a complicated technique; it is becoming even more complicated as advancements in generation have furnished tools to make the gathering, employing, and evaluating information less complicated and faster. Research techniques, including artificial intelligence, neural networks, and machine learning, can speed up records retrieval and improve outcomes' accuracy. This look examines the different machine learning techniques to optimize the facts retrieval process and the advantages and downsides of each technique. Furthermore, this study looks at how gadgetstudying algorithms improve a person's enjoyment of retrieval systems and how their use can be integrated into cutting-edge practices. Subsequently, methods for improving the performance of machine learning algorithms are discussed in addition to capacity destiny directions inside the use of gadget studying to optimize statistics retrieval.

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Exploring the Security Implications of Stream and Block Ciphers in Cryptography and Network Security

Exploring the Security Implications of Stream and Block Ciph...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Dipannita Mondal S. Hemelatha Jayadurga R Nakul S. Sharma Sidharth Jain Jambi Ratna Raja Kumar Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India

This paper examines the security implications of movement and block ciphers in cryptography and network security. Circulate ciphers encrypt facts as they are transmitted over a network. A block cipher encrypts and decrypts a set amount of information. The paper discusses how these types of cipher paintings and opinions affect data security. Additionally, the paper looks at several demanding situations associated with using them, along with crucial changes, block size, critical duration, and overall performance. The paper provides a complete assessment of the pros and cons of circulate and block ciphers and gives pointers for which sort of cipher to apply in one-of-a-kind situations.

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$\mathrm{D}^{3}$T: Deep Denoising Dictionary Tensor for Hyperspectral Anomaly Detection

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 18卷 3713-3727页

作者： Qiangqiang Shen Zonglin Liu Hanzhang Wang Yanhui Xu Yongyong Chen Yongsheng Liang School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Science Harbin Institute of Technology Shenzhen China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen Guangdong China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen Guangdong China

Hyperspectral anomaly detection (HAD) approaches with tensor low-rank representation (TLRR) have shown engaging performance, which are capable of capturing abundant spectral and spatial information and, hence, extracting sparse anomalies from the low-rank background without ruining the intrinsic geometric structures of tensor data. However, existing HAD approaches construct the low-rank representation by a handcrafted dictionary that still contains the anomalies, resulting in an inferior representation. To this end, we propose a deep denoising dictionary tensor for hyperspectral anomaly detection ( $\mathrm{D}^{3}$ T), which can balance performance and interpretability by fusing two intuitive priors, i.e., low-rankness and deep denoising, into the dictionary tensor and coefficient tensor. In particular, the proposed $\mathrm{D}^{3}$ T first designs a tensor recovery module for dividing the low-rank background and sparse anomalies, where the background is optimized as a denoising dictionary tensor by the joint low-rankness and deep denoising. Following that, we build the TLRR model based on the denoising dictionary tensor to explore the latent representation of the input hyperspectral images, and hence, each background pixel is represented by the other clear background pixels in place of the anomaly pixels. Meanwhile, the coefficient tensor in TLRR is also optimized by the two priors to fully explore the spatial and spectral correlations in the background, and hence, the anomalies can be accurately detected. Numerous experimental results from various real-world datasets validate the effectiveness of our $\mathrm{D}^{3}$ T.

关键词： Tensors Dictionaries Noise reduction Anomaly detection Hyperspectral imaging Sparse matrices Noise Principal component analysis Feature extraction Electronic mail

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Comprehensive Analysis of Machine Learning Approaches for Phishing Website Detection

Comprehensive Analysis of Machine Learning Approaches for Ph...

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Cognitive Computing in Engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Pragna K P Suhani Shetty Ashwini Kodipalli Ushasree. A Shridhar B. Devamane Roshan Joy Martis Dept. of Electronics and Communication Engineering Global Academy Of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of Computer Science School of Computing Amrita Vishwavidyapeetam Mysuru Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Website phishing, a notable cybercrime, involves deceiving individuals by impersonating legitimate websites. URL spoofing, a common technique in website phishing, involves creating website names. The primary objective of this paper is to accurately differentiate between legitimate and phishing sites using relevant URL attributes and different binary classification machine learning algorithms. This study compares simple, bagging, and boosting algorithms across performance analysis metrics. The best-performing models are selected for an ensemble approach. Additionally, various hyperparameter tuning techniques are assessed for the Random Forest Classifier, and Explainable AI methods like LIME and SHAP are used to interpret findings. The experiment yields optimal parameters, achieving 89. % accuracy with an ensemble model with K-Nearest Neighbors, Decision Tree, XG Boost, and Cat Boost. These findings highlight the significance of ensemble models and hyperparameter tuning in machine learning.

关键词： Uniform resource locators Machine learning algorithms Explainable AI Phishing Computational modeling Biological system modeling Nearest neighbor methods Performance analysis Tuning Random forests

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Diagnosis of Faults in Edge-PLC using Optimized Deep Learning models in Industrial IoT

Diagnosis of Faults in Edge-PLC using Optimized Deep Learnin...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： A. Kishore Kumar T Rajesh Kumar R. Balamanigandan A. Sasi Kumar Mahaveerakannan R Department of Robotics and Automation Sri Ramakrishna Engineering College Coimbatore Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department Of Cloud Technology & Data Science Institute Of Engineering & Technology Srinivas University Surathkal

Industrial Internet of Things (IIoT) may allow for the state of equipment to be monitored and defects to be detected before they become serious, helping smart factories keep up with the rising demands for safety and efficiency brought on by worldwide competition. In the case of Cloud Computing, data-driven approaches implemented in the cloud are used to first train the model using a vast quantity of raw data, and then end-machines send their real-time readings for processing at the cloud data centre. It is possible, however, that this will impose a significant delay and a high computational cost. In this study, we take into account a hierarchical structure in which edge-PLCs are used to collect sensed data locally and lessen the burden on long-distance connections. Due to the fact that a single responsibility may be associated with multiple swaying topographies, we aim to first reduce the sum of features required to identify a fault, and then identify the smallest possible set of edge-PLCs that still accounts for all of the most important ones, all while keeping deployment costs to a minimum. To address this, we present an Enhanced Harris Hawkins Optimization (EHHO) approach to tackling the features selection issue, followed by the assortment of edge-PLCs through the set coverage problematic. The model named Residual Attention Neural Network (RANN) is proposed the in this study to forecast the fault detection procedure. Residual Neural Network (ResNet) is used by RANN to help diagnose problems, alongside the knowledge of experts. We evaluate the performance of our technique in comparison to existing AI-based approaches, such as the lregression model and its expansions, by simulating on a real data trail. The findings confirm competence and presentation of the suggested approach, which meets or even surpasses the correctness of approaches employing the entire collection of data.

关键词： Fault diagnosis Deep learning Solid modeling Cloud computing Image edge detection Computational modeling Fault detection

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Better Quantum Seal Schemes based on Trapdoor Claw-Free Functions

arXiv

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arXiv 2023年

作者： Cheng, Xiao-Gang Guo, Ren College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China College of Business Administration Huaqiao University Quanzhou362021 China

Seal in classical information is simply impossible. Since classical information can be easily copied any number of times. Based on quantum information, esp. quantum unclonable theorem, quantum seal maybe constructed perfectly. But it is shown that perfect quantum seal is impossible, and the success probability is bounded. In this paper, we show how to exceed the optimal bound by using the TCF (Trapdoor Claw Free) functions, which can be constructed based on LWE assumption. Hence it is post-quantum secure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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PAIRWISE REARRANGEMENT IS FIXED-PARAMETER TRACTABLE IN THE SINGLE CUT-AND-JOIN MODEL

arXiv

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arXiv 2024年

作者： Bailey, Lora Blake, Heather Smith Cochran, Garner Fox, Nathan Levet, Michael Mahmoud, Reem Singgih, Inne Stadnyk, Grace Wiedemann, Alexander Department of Mathematics Grand Valley State University AllendaleMI United States Department of Mathematics and Computer Science Davidson College DavidsonNC United States Department of Mathematics and Computer Science Berry College Mount BerryGA United States Department of Quantitative Sciences Canisius University BuffaloNY United States Department of Computer Science College of Charleston CharlestonSC United States Division of Science NYU Abu Dhabi Abu Dhabi United Arab Emirates Department of Mathematical Sciences University of Cincinnati CincinnatiOH United States Department of Mathematics Furman University GreenvilleSC United States Department of Mathematics and Computational Data Science Hamline University Saint PaulMN United States

Genome rearrangement is a common model for molecular evolution. In this paper, we consider the Pairwise Rearrangement problem, which takes as input two genomes and asks for the number of minimum-length sequences of permissible operations transforming the first genome into the second. In the Single Cut-and-Join model (Bergeron, Medvedev, & Stoye, J. Comput. Biol. 2010), Pairwise Rearrangement is #P-complete (Bailey, et. al., COCOON 2023;Theor. Comput. Sci. 2024), which implies that exact sampling is intractable. In order to cope with this intractability, we investigate the parameterized complexity of this problem. We exhibit a fixed-parameter tractable algorithm with respect to the number of components in the adjacency graph that are not cycles of length 2 or paths of length 1. As a consequence, we obtain that Pairwise Rearrangement in the Single Cut-and-Join model is fixed-parameter tractable by distance. Our results suggest that the number of nontrivial components in the adjacency graph serves as the key obstacle for efficient *** Codes 92-08, 92D10, 92D20, 68Q17 © 2024, CC BY.

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An IoT and Random Forest-DT-Based Early Enhanced Forest Fire Detection in Large and Small Targets

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Manjula Prabakaran Omesh Wadhwani V. Pandiaraj Kaneez Zainab Neerav Nishant Justin Z Department of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science & Engineering B.N. College of Engineering and Technology Lucknow India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India Department of Science and Humanities (English) RMK College of Engineering and Technology Thiruvalluvar India

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth’s climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LBP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach.

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