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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Sudeep Sharan Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 11519 Puerto Real Cádiz Spain

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants.

关键词： Ant colony optimization Signal processing algorithms Signal processing Path planning Computational efficiency Robots Optimization

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Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Solving Time-Independent Linear Matrix-Vector Equation (TILMVE) Simplified from Time-Dependent Situation

Discrete RKZD (Reciprocal-Kind Zhang Dynamics) Algorithm Sol...

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Chinese Control Conference (CCC)

作者： Yunong Zhang Ji Lu Mingzhi Mao Shuai Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou P. R. China School of Software Engineering Sun Yat-sen University Zhuhai P. R. China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland

We present a continuous reciprocal-kind Zhang dynamics (RKZD) model for solving the time-dependent linear matrixvector equation. On the basis of the model, we deduce its simplified form for solving the time-independent linear matrix-vector equation (TILMVE). Subsequently, for more efficient computation and easier implementation in digital hardware, we utilize Euler forward difference formula (EFDF) to discretize the continuous RKZD model, resulting in a discrete RKZD algorithm. Finally, numerical experimental results attest to the feasibility and high effectiveness of the discrete RKZD algorithm for solving TILMVE. Comparisons with the discrete gradient neural network (or termed discrete gradient dynamics), Jacobi iteration, as well as Gauss-Seidel iteration highlight the superior convergence properties of the discrete RKZD algorithm.

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Why There Are Still Moral Reasons to Prefer Extended over Embedded: a (Short) Reply to Cassinadri

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Philosophy & Technology 2022年第3期35卷 1-7页

作者： Farina, Mirko Lavazza, Andrea Faculty of Humanities and Social Sciences Innopolis University Universitetskaya St 1 Innopolis Russian Federation Lab of Industrializing Software Production (LIPS) Faculty of Computer Science and Engineering Universitetskaya St 1 Innopolis Russian Federation Centro Universitario Internazionale Arezzo Italy

In a recent paper, Cassinadri (2022) raised substantial criticism about the possibility of using moral reasons to endorse the hypothesis of extended cognition (EXT) over its most popular alternative, the embedded view (EMB). In particular, Cassinadri criticized 4 of the arguments we formulated to defend EXT and argued that our claim that EXT might be preferable to EMB (on the grounds of its progressiveness and inclusiveness) does not stand close scrutiny. In this short reply, we point out—contra Cassinadri—why we still believe that there are moral reasons to prefer EXT over EMB, hence why we think that the former is more inclusive and more progressive than the latter.

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Linking network- and neuron-level correlations by renormalized field theory

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Physical Review Research 2024年第3期6卷 033264页

作者： Michael Dick Alexander van Meegen Moritz Helias Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10) Department of Computer Science 3 - Software Engineering Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1) Institute of Zoology Department of Physics Faculty 1

It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.

关键词： Neuronal network activity Statistical field theory Cortical networks Critical phenomena Dynamical mean field theory

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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A Systematic Literature Review of Studies Related to Mental Activities of software Developers

A Systematic Literature Review of Studies Related to Mental ...

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2020 International Conference Nonlinearity, Information and Robotics, NIR 2020

作者： Sirazitdinov, Ilyas Succi, Giancarlo Ivanov, Vladimir Philips Research Philips Moscow Russia Innopolis University Faculty of Computer Science and Software Engineering Innopolis Russia

ISBN: (纸本)9781728187631

In this paper we have performed a systematic review of studies related to the mental states of people engaged in software development, particularly programmers. This review underscores consensus relating to some more thoroughly studied areas, such as the relationship between emotional state and productivity. It also highlights emerging trends such as the use of biosensors, and many areas of limited understanding that might be fruitful topics for the application of new emerging techniques. © 2020 IEEE.

关键词： software design

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A Framework for Secure Communication on Internet of Things (IoT) 5th

A Framework for Secure Communication on Internet of Things (...

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5th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2020

作者： Hosenkhan, Mohammad Reza Pattanayak, Binod Kumar Department of Software Engineering Faculty of Information and Communication Technology Universite Des Mascareignes Beau Bassin-Rose Hill Mauritius Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (纸本)9789813342989

Internet of things (IoT) encompasses millions of resource constrained wirelessly connected devices that are very often prone to external malicious attacks. Hence, there arises a need for protecting these devices from such attacks. In this paper, we have proposed a secure communication scheme for resource constrained scheme where a device is identified by a key pair, public key and private key, and its unique identification (ID) that is assigned to it by Google Cloud IoT Core and the device stores the private key for communication with the server. Key generation uses a cryptographic approach. The communication can be facilitated by the device communicating its private key along with a cryptographically signed Java-based Web Token (JWT) to the MQTT bridge following which the connection can be established. The same JWT can be used for further communication as well. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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International Conference on Emerging Technologies, ICET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

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A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

A Hybrid Method for Analyzing the Situation Based on Cumulat...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Marzia Ahmed Mohd Herwan Sulaiman Ahmad Johari Mohamad Mostafijur Rahman Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang (UMP) Pahang Malaysia Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters.

关键词： COVID-19 Support vector machines Pandemics Soft sensors Time series analysis Predictive models Mathematical models

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An Efficient Pattern Recognition Based Method for Drug-Drug Interaction Diagnosis 1

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Javed, Rabia Saba, Tanzila Humdullah, Salman Mohd Jamail, Nor Shahida Javed Awan, Mazhar Lahore College for Women University Computer Science Department Lahore54300 Pakistan Prince Sultan University Computer and Information Science Riyadh11586 Saudi Arabia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia University of Management and Technology Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9780738131771

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier. © 2021 IEEE.

关键词： Diagnosis

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