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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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Physique- Based Human Activity Recognition Using Deep Learning Approaches and Smartphone Sensors 8

Physique- Based Human Activity Recognition Using Deep Learni...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Pravesjit, Sakkayaphop Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Information Technology University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Understanding human actions via the analysis of sensor data captured by wearable sensors is the goal of the complex subject of study known as sensor-based human activity recognition (S-HAR). Human participants' characteristics are only periodically included in deep learning (DL) approaches to S-HAR. Recognizing people was challenging for these DL methods because of the variety of physical characteristics people have. To address this challenge, we introduce a physique-based S-HAR architecture that could support deep learning networks to achieve higher identification a ccuracies a nd F1-scores. The HARSense dataset, a publicly available benchmark S-HAR dataset that compiles raw sensor data acquired from smartphones, was employed to build and evaluate five DL networks. A ccording to the experiments, the five models' detection performance improves dramatically when given access to biological data. © 2023 IEEE.

关键词： Classification (of information)

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Advancement in Agronomy Using Machine Learning Approach 7

Advancement in Agronomy Using Machine Learning Approach

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Patel, Darshana H Mirani, Hemangee B. Vasant, Avani R. Chaudhari, Nandini M. Vvp Engineering College Department of Information Technology Rajkot India Computer Science and Engineering Krishna School of Emerging Technology and Applied Research Drs Vadodara India

ISBN: (纸本)9781665421683

Agronomy is the science of dealing with the distribution, management, and productivity of agricultural land. When living in a country where agriculture is the heart of the economy and people's lifestyle, agronomy and the development in agronomy becomes a major responsibility of IT sectors to seek enhancement in existing technologies. All the factors of agronomy are so much dependent on rainfall in India, where unpredictability of monsoon and uneven distribution of rainfall leaves us with the next best option - Irrigation! The key aspect of this paper is to select best suitable solutions from the major irrigation systems namely drip irrigation, surface irrigation, sprinkler irrigation and sub-surface irrigation. Machine learning comes to rescue as the model is trained with thousands of samples of data along with the best irrigation system suitable for respective location. The location of a farm or field is selected using illustrator nominator wherein appropriate crop recommendation report can be generated to improve the productivity using machine learning algorithms. Thus, machine learning is on its way to help the farmers to adapt suitable system according to their requirements. © 2022 IEEE.

关键词： Crops

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Improving Smart Agriculture Through the Use of Machine Learning to Offer Tailored Crop Forecasts

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 74-83页

作者： Patil, R.N. Maroor, Jnaneshwar Pai Dhanke, Jyoti A. Chunduru, Madhava Rao Singh, Mangal Jangir, Pradeep Upadhyay, Shrikant Department of Mechanical Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department Of Computer Science And Engineering Koneru Lakshmaiah Educational Foundation Vaddeswaram AP Guntur District India Department of Electronics and Telecommunication Engg. Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

It is not possible to identify crop illnesses by examining each disease independently. Users may only get forecasts for the most anticipated illnesses with the use of a robust analytic methodology. This study presents a method that can collect, analyze, and identify crop health information all on one platform using an IoT and machine learning-based approach. Food security and the provision of essential raw materials for several industries are both supported by agriculture, which is the most important and basic vocation. Improved crop yields, thanks to new innovative agricultural practices, mean less water wasted and more money in the bank. An intelligent irrigation system that uses a machine learning algorithm to forecast a crop’s water needs is the suggested model. One such rapidly evolving invention is the Internet of Things (IoT), which is now spreading its wings across all geographies. Technological innovation is reaching a new level of practicality in agriculture with the rise of embedded PCs like Arduino. Soil quality monitoring using Arduino, several sensors, and an Android app is shown and accomplished in this study. Temperature, soil moisture level, ammonia, and carbon content limit soil quality in this study. Arduino is used as both a worker and an information processing device, and it is in charge of sensor security. The terminal device is an Android phone. Producers may improve agricultural yields by collecting and analyzing data on weather, soil, seed, and yields of crops, moisture, and gusts of wind in specific areas. Furthermore, a flask-built GUI was used to present crop forecasts derived from a bespoke recommender system. Because of its adaptable design, the system may one day be able to discover the suggested items of other areas. This article uses targeted agricultural data to create five machine-learning approaches that are comparable to one another and to compare and contrast them. © The Author(s), under exclusive licence to Springer Nature Swit

关键词： Decision navigation systems Internet of Things (IoT) Irrigation systems Machine learning algorithms Sensors

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KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

KGIF: Optimizing Relation-Aware Recommendations with Knowled...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Jeon, Dong Hyun Sun, Wenbo Song, Houbing Herbert Liu, Dongfang Alvaro, Velasquez Xie, Yixin Chloe Niu, Shuteng Bowling Green State University Department of Computer Science United States University of Michigan Transportation Research Institute Ann Arbor United States University of Maryland Baltimore County Department of Information Systems United States Rochester Institute of Technology Department of Computer Engineering United States University of Colorado College of Engineering & Applied Science Boulder United States Kennesaw State University Department of Information Technology United States

ISBN: (纸本)9798350362480

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances *** study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1 © 2024 IEEE.

关键词： Knowledge graph

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A Mobile App Game Based on the Development and Design of a Puzzle Created for Educational Learning 13th

A Mobile App Game Based on the Development and Design of a P...

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13th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2023

作者： Sapundzhi, Fatima Kitanov, Anton Lazarova, Meglena Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" 66 Ivan Myhailov Street Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Mathematics and Infromatics Bulgarian Academy of Sciences 8 Acad. Georgi Bonchev Street Sofia1113 Bulgaria Department of Applied Mathematics and Statistics University of Ruse "Angel Kanchev" 8 Studentska Street Ruse7004 Bulgaria

ISBN: (纸本)9783031421334

This paper presents a mobile app game designed for educational learning. The game is created by using contemporary software tools, featuring five different puzzle levels in which players have to solve jigsaw puzzles and eventually the player learns an interesting fact about the scene they arranged. The game aims to improve the young player’s spatial imagination, problem-solving skills, memory, patience, concentration and motor skills. In this study the steps how to develop and implement the game are thoroughly explained. The article highlights the importance of the educational games and their potential to enhance learning outcomes in a fun and engaging way. The game’s development process and design are discussed along with its potential to be utilized as an educational tool in various settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： E-learning

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Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

Classification of Physical Exercise Activity from ECG, PPG a...

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2022 research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts. © 2022 IEEE.

关键词： Electrocardiograms

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Statistical Mechanics and Pressure of Composite Optical Systems

Statistical Mechanics and Pressure of Composite Optical Syst...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nikolaos K. Efremidis Demetrios N. Christodoulides Department of Applied Mathematics University of Crete Heraklion Greece Institute of Applied and Computational Mathematics FORTH Heraklion Crete Greece Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles California USA

We develop a thermodynamic theory that takes into account the synergistic action of multiple components. We compute the optomechanical pressure and find that the type of the nonlinearity involved can lead to different... 详细信息

ISBN: (纸本)9798350369311

关键词： Thermodynamics Laser theory Lasers and electrooptics Electro-optical waveguides

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Charged particles

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An Adaptive Streaming Feature Selection Technique for Classifying Non-Stationary Data Streams 7

An Adaptive Streaming Feature Selection Technique for Classi...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Arya, Monika Dewangan, Bhupesh Kumar Choudhury, Tanupriya Kotecha, Ketan Dewangan, Sanjana Gaidhane, Akhilesh Bhilai Institute of Technology Department of Computer Science and Engineering Durg India Op Jindal University Department of Computer Science and Engineering Raigarh496109 India Pune411045 India Symbiosis Centre for Applied Artificial Intelligence Pune411045 India School of Science Op Jindal University Raigarh India Sages M.G. Ward Department of Mathematics Kondagaon India

ISBN: (纸本)9798350342154

In today's digital era, various real-world applications generate data in streams, and these data streams are of two types stationary data streams, which are static, and non-stationary data streams that are dynamic in nature. Non-stationary data streams are pro-to-concept drifts. To overcome this issue, an adaptable streaming feature selection technique for non-stationary data streams is proposed in this paper. The selection of relevant feature space from non-stationary data streams is crucial for enabling intelligence through various data mining tasks, specifically using data classification. Feature selection improves the overall performance of the classifier while increasing the computational overhead. The proposed approach aims to enable streaming feature selection whenever the classification accuracy is underperforming to enhance the classification accuracy, while the model remains stable if there is no change in the concepts. The proposed approach is adaptive and ameliorates the classifier performance by reducing the error rate during changes in the concept with time. This adaptability to concept drift improves the accuracy by approximately 16% and reduces the computation time by 50%. The proposed model avoids overfitting of the model. © 2023 IEEE.

关键词： Feature Selection

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