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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Rahman, Md Saifur Rifat, Nafiz Ahsan, Mostofa Islam, Sabrina Chowdhury, Md. Gomes, Rahul University of Minnesota Crookston Dept. of Math Science and Technology CrookstonMN United States North Dakota State University Dept. of Computer Science FargoND United States Florida Atlantic University Dept. of Biological Sciences Boca RatonFL United States East Stroudsburg University Dept. of Computer Science East StroudsburgPA United States University of Wisconsin-Eau Claire Dept. of Computer Science Eau ClaireWI United States

ISBN: (纸本)9781665493765

Malaria continues to be a significant burden on global health with 247 million clinical episodes and 619,000 deaths. Along with biomedical science, technology, and informatics have begun participating in the quest against malaria. Microscopy techniques are frequently used to detect malaria parasites in infected red blood cells. Giemsa stain has been used to stain blood parasites for over a century. The stain is applied after fixing blood smears in methyl alcohol for 25 to 30 minutes [1]. When stained slides are examined under a microscope, the parasites are easily discernible based on morphology and color. We observed that automating the detection of these slides using deep learning is possible with high accuracy. A comparison between deep learning models reveals ResNets provide better performance. © 2023 IEEE.

关键词： Diseases

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Partial Product Updates for Agents of Detectable Failure and Logical Obstruction to Task Solvability

arXiv

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arXiv 2023年

作者： Nakai, Daisuke Muramatsu, Masaki Nishimura, Susumu Dept. Math. Graduate School of Science Kyoto University Japan

The logical method proposed by Goubault, Ledent, and Rajsbaum provides a novel way to show the unsolvability of distributed tasks by means of a logical obstruction, which is an epistemic logic formula describing the reason of unsolvability. In this paper, we introduce the notion of partial product update, which refines that of product update in the original logical method, to encompass distributed tasks and protocols modeled by impure simplicial complexes. With this extended notion of partial product update, the original logical method is generalized so that it allows the application of logical obstruction to show unsolvability results in a distributed environment where the failure of agents is detectable. We demonstrate the use of the logical method by giving a concrete logical obstruction and showing that the consensus task is unsolvable by the single-round synchronous message-passing protocol. © 2023, CC BY.

关键词： Message passing

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A New Clustering Approach based on Trust and Rat Swarm Algorithm for WSN Applications

A New Clustering Approach based on Trust and Rat Swarm Algor...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Osamy, Walid Salim, Ahmed Ali, Amel Al Khedr, Ahmed M. Faculty of Computers and Artificial Intelligence Benha University Computer Science Department Benha Egypt Zagazig University Math. Department Faculty of Science Zagazig Egypt Qassim University Department of Cs College of Sci. and Arts Al-Methnab Saudi Arabia Sharjah University Dept. of Information Systems Sharjah27272 United Arab Emirates Sharjah University Dept. of Computer Science Sharjah27272 United Arab Emirates

ISBN: (纸本)9798350333817

In this paper, a clustering approach called CATRSO is proposed. The selection of cluster heads (CH) is performed by considering the trust value of the nodes in order to select the most trustworthy nodes as CH and Rat Swarm Optimizer is employed for CH selection process. The trust value of the nodes and remaining energy are taken into account while designing the fitness function. In addition, a chain routing approach is employed between CHs for energy savings. The results demonstrate that the CATRSO technique is successful in selecting the most trustworthy nodes as CH and outperforms earlier efforts in the literature in terms of energy efficiency, average network lifetime, and trustworthiness of selected CHs. © 2023 IEEE.

关键词： Rats

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Histopathological Cancer Detection with Topological Signatures

Histopathological Cancer Detection with Topological Signatur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ankur Yadav Faisal Ahmed Ovidiu Daescu Reyhan Gedik Baris Coskunuzer Dept. of Computer Science U. Texas at Dallas Dept. of Math. Sciences U. Texas at Dallas Dept. of Pathology Harvard - MGH

We present a transformative approach to histopathological cancer detection and grading by introducing a very powerful feature extraction method based on the latest topological data analysis tools. By analyzing the evolution of topological patterns in different color channels, we discovered that every tumor class leaves its own topological footprint in histopathological images, allowing to extract feature vectors that can be used to reliably identify tumor *** topological signatures, even when combined with traditional machine learning methods, provide very fast and highly accurate results in various settings. While most DL models work well for one type of cancer, our model easily adapts to different scenarios, and consistently gives highly competitive results with the state-of-the-art models on benchmark datasets across multiple cancer types including bone, colon, breast, cervical (cytopathology), and prostate cancer. Unlike most DL models, our proposed Topo-ML model does not need any data augmentation or pre-processing steps and works perfectly on small datasets. The model is computationally very efficient, with end-to-end processing taking only a few hours for datasets consisting of thousands of images.

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Exploring Mixed-Grid Environments for Drone-Based Last-Mile Logistics Optimization

Exploring Mixed-Grid Environments for Drone-Based Last-Mile ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Francesco Betti Sorbelli Federico Corò Cristina M. Pinotti Anil Shende Dept. of Computer Science and Mathematics University of Perugia Italy Dept. of Mathematics University of Padua Italy Dept. of Math. CS & Physics Roanoke College

In recent years, drone-based delivery systems have gained significant attention for last-mile logistics in various domains. This study focuses on optimizing drone deliveries in mixed-grid environments, which combine urban and rural areas. The objective is to minimize the sum of distances between the delivery locations and the drone’s depot. By selecting the optimal depot placement, we aim to improve factors such as expected delivery time, energy consumption, and environmental impacts. To address this optimization problem, we conducted a comprehensive comparison of several state-of-the-art algorithms specifically designed for computing the drone placement in mixed-grid environments. Our evaluation utilized synthetic and quasi-real data and two distinct scenarios were considered: the full-grid scenario, and the partial-grid scenario. Through empirical analyses, we demonstrate the effectiveness of the proposed algorithms and provide valuable insights into their trade-offs in terms of performance and time complexity.

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Risks in Blockchain - A Survey about Recent Attacks with Mitigation Methods and Solutions for Overall

Risks in Blockchain - A Survey about Recent Attacks with Mit...

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2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Mrazek, Kristian Holton, Brian Cathcart, Charles Speirer, Jacob Do, John Mohd, Tauheed Khan Augustana College Dept. of Math and Computer Science Rock Island United States

ISBN: (纸本)9781665480093

The purpose of this paper is to analyze the context and attack surface of a variety of security breaches and attacks on cryptocurrency blockchains. For many different types of blockchain attacks, such as distributed denial-of-service (DDoS) attacks and majority (51%) attacks, there is very little literature investigating and comparing particular attack instances as opposed to focusing on general theoretical studies and mitigation techniques. The attacks investigated here include a series of DDoS attacks on the Bitfinex cryptocurrency exchange and a group of instances of the 51% attack on thirteen different cryptocurrency blockchains. The goal of this research is to find traits in the context and attack surface that exacerbate the losses caused by certain attacks as opposed to others. It will then propose solutions and mitigation techniques based on the circumstances of the cases where the impact of the attack was less serious. © 2022 IEEE.

关键词： Blockchain

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Analysis of Scanline and Minimum Entropy Selection Heuristics in Model Synthesis and Wave Function Collapse

Analysis of Scanline and Minimum Entropy Selection Heuristic...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Lucas Harvey Gautam Srivastava Dept. of Math and Computer Science Brandon University Canada

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

In this paper, we analyze two versions of a texture synthesis algorithm, study the cases in which they fail to produce a successful result and present modifications that could be made to lessen their rates of failure. This algorithm, Model Synthesis, and its variation Wave Function Collapse are designed to take in a small sample input image, or set of image constraints, and produce a larger pseudorandom output image in which every region of the output image is locally similar to an element of the input image. Both versions of the algorithm accomplish this task by considering their output image as a grid of cells with each cell initially in a superposition of all possibilities for itself and resolving cells one by one until all cells have been resolved from their superposition to a fixed value. One of the key differences between the two versions of the algorithm is the order in which cells are selected to be resolved, one simply selects in a scanline order, while the other resolves first those cells that have the minimum entropy, and thus which we can be most certain of their eventual state. For many inputs, the minimum entropy model reaches a state in which its output is not consistent with the input and thus fails, while the scanline model does not. This paper looks at the cases in which this occurs and concludes that this is often caused by the minimum entropy model creating regions of elevated constraints in its solution. Finally, it presents a possible alteration to the algorithm which allows a minimum entropy model to avoid this manner of failure among a subset of test cases.

关键词： Analytical models Image resolution Heuristic algorithms Computational modeling Entropy Wave functions

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Optimal Local Parameters For Hermite Type Cubic Splines

Optimal Local Parameters For Hermite Type Cubic Splines

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International Conference on Engineering of Modern Electric Systems (EMES)

作者： Alexandru Mihai Bica Eugen Laslo Cristian G. Crainic Dept. of Math. and Computer Science University of Oradea Oradea Romania Dept. of Forestry and Forest Eng. University of Oradea Oradea Romania

The local derivatives of Hermite type cubic splines are optimally determined on each subinterval of a given mesh through a set of five classical interpolation procedures in order to minimize a certain objective function. We construct an iterative method that selects on each subinterval, the adequate algorithm for providing the local derivatives which minimize the objective function such that the values of the local derivatives computed at the previous subinterval are preserved in order to keep the smoothness of the spline. The admissible set of classical interpolation procedures consists of the natural cubic spline, the Catmull-Rom cubic spline, the Akima's cubic spline, and the cubic splines with minimal derivative oscillation and those with minimal deviation by the data polygon. A numerical experiment is presented in order to illustrate the performances of the algorithm.

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Collaborative Intrusion Detection System for Intermittent 10 Vs Using Federated Learning and Deep Swarm Particle Optimization 11

Collaborative Intrusion Detection System for Intermittent 10...

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11th IEEE International Conference on Data science and Advanced Analytics, DSAA 2024

作者： Ullah, Farhan Srivastava, Gautam Mostarda, Leonardo Cacciagrano, Diletta University of Camerino Division of Computer Science Camerino Italy Brandon University Dept. of Math and Computer Science BrandonMBR7A 6A9 Canada University of Perugia Department of Mathematics and Computer Science Perugia Italy School of Software Northwestern Polytechnical University Xian China

ISBN: (纸本)9798350364941

Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance. © 2024 IEEE.

关键词： Federated learning

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Exploring Neutrosophic Numeral System Algorithms for Handling Uncertainty and Ambiguity in Numerical Data: An Overview and Future Directions

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Neutrosophic Sets and Systems 2024年 65卷 253-295页

作者： Salama, A.A. Shams, Mahmoud Y. Elseuofi, Sherif Khalid, Huda E. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Information System Department Higher Institute for Computers & Specific Studies Ras-Elbar Damietta Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq

The Neutrosophic Numeral System Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a math.matical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral System Algorithms, including Neutrosophic Binary System, Neutrosophic Decimal System, and Neutrosophic Octal System. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral System Algorithms and Number Neutrosophic Systems, which are another type of Neutrosophic System used for representing numerical data. Number Neutrosophic Systems use Neutrosophic Numbers to represent degrees of truth, indeterminacy, and falsity in numerical data, and they can be used in conjunction with Neutrosophic Numeral System Algorithms to handle uncertainty and ambiguity in decision-making and artificial intelligence applications. Moreover. We discuss the advantages and disadvantages of each algorithm and their potential applications in various fields. Finally, we highlight the importance of Neutrosophic cryptography in addressing uncertainty and ambiguity in decision making and artificial intelligence and discuss future research directions. Understanding Neutrosophic Numeral System Algorithms and their relationship with Number Neutrosophic Systems is crucial for developing effective techniques for handling uncertainty and ambiguity in numerical data in decision-making, pattern recognition, and artificial intelligence applications. © (2024) All Rights Reserved.

关键词： Decision making

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