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A Smart Robotic Design to Identify and Diffuse Bombs in Defence Environments Using Sensors Assisted Internet of Things (IoT) Technology

A Smart Robotic Design to Identify and Diffuse Bombs in Defe...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Vijayakumari, P. Mary, S. Jeya Deepa, N. Swarna, M. Narkhede, Alok Al-Safarini, Maram Y. Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Dept of Ece Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India Dr. M.G.R. Educational and Research Institute University Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Numerous news media outlets cover incidents in which trained personnel or combatants are injured or perish while defusing bombs. The military robot that conducts intelligence operations. While it has been beneficial to the Army, firefighters, and police officers in critical situations, many of the current systems are inefficient due to their lack of accuracy and technology. Our objective is to develop the most efficient robot possible in order to achieve accuracy and technology. Robotic limbs equipped with metal detectors are being installed to provide manual assistance. The wireless explosive disposal device will enhance our nation's defense against terrorists, suicide bombers, and other similar activities. Experts are required to operate the explosives detectors and disposal system. However, this method of analysis necessitates additional time and poses a risk to the lives of experts. A novel robot, the IoT Defense Robotic Model (IoTDRM), is developed in this paper with the assistance of Internet of Things (IoT) technology. The proposed model is cross-validated against the conventional logic of the Smart Bomb Diffusing Robot (SBDR) to assess its performance. The signals are transmitted from the sensor end to the IoT controller in this robot, and they are retransmitted to the server end via a wireless system. The user controls the automaton manually using a personal computer or mobile phone. When the sensor detects a metal, a buzzer alarm will sound. The metal is examined using a wireless camera to determine whether it is a weapon. Since the identified metal is a bomb, the user instructs the robot to detonate it via the computer. © 2024 IEEE.

关键词： Bombs (ordnance)

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Exploiting Deep Learning Architectures for Effective Real-Time Data Analysis in Machine Learning 3

Exploiting Deep Learning Architectures for Effective Real-Ti...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreenidhi, H.S. Rajarajeswari, S. Bhagwat, Suvarna R. Sharma, Khushboo Seth, Kanika Yadav, Rakesh Kumar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper seeks to discover how deep state-of-the-art architectures can be leveraged for robust actual-time information evaluation in device studying packages. The paper begins by supplying a comprehensive assessment of contemporary the modern-day in deep brand new-based totally facts evaluation technology. It then examines case research state-of-the-art real-time records analysis programs to illustrate the viability and blessings of modern-day existing deep latest architectures. Programs encompass self-driving vehicle generation, laptop vision, natural language processing, and time-collection evaluation. This targeted evaluation is supplemented by assessing ultra-modern capability shortcomings and demanding situations associated with using these architectures in actual-time scenarios. Subsequently, the paper provides a set of modern-day pointers and first-rate practices to help practitioners efficiently leverage deep trendy architectures for actual-time information evaluation. With the aid of combining a comprehensive review of current deep-gaining knowledge state modern-based records evaluation technology, a detailed dialogue present day its utility guiding ideas, and a summary of the latest potential shortcomings and demanding situations, this paper offers a practical manual to the effective of deep learning architectures for real-time data analysis. © 2023 IEEE.

关键词： architectures challenges combining recommendations shortcomings

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Arabic Dialect Identification in Social Media:A Comparative Study of Deep Learning and Transformer Approaches

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Intelligent Automation & Soft Computing 2024年第5期39卷 907-928页

作者： Enas Yahya Alqulaity Wael M.S.Yafooz Abdullah Alourani Ayman Jaradat Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityBuraydah51452Saudi Arabia Internet of Things Department Faculty of Science and Information TechnologyJadara UniversityIrbid21110Jordan

Arabic dialect identification is essential in Natural Language Processing(NLP)and forms a critical component of applications such as machine translation,sentiment analysis,and cross-language text *** difficulties in differentiating between Arabic dialects have garnered more attention in the last 10 years,particularly in social *** difficulties result from the overlapping vocabulary of the dialects,the fluidity of online language use,and the difficulties in telling apart dialects that are closely *** dialects with limited resources and adjusting to the ever-changing linguistic trends on social media platforms present additional challenges.A strong dialect recognition technique is essential to improving communication technology and cross-cultural understanding in light of the increase in social media *** distinguish Arabic dialects on social media,this research suggests a hybrid Deep Learning(DL)*** Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM)architectures make up the model.A new textual dataset that focuses on three main dialects,i.e.,Levantine,Saudi,and Egyptian,is also *** 11,000 user-generated comments from Twitter are included in this dataset,which has been painstakingly annotated to guarantee accuracy in dialect ***,DL models,and basic machine learning classifiers are used to conduct several tests to evaluate the performance of the suggested *** methodologies,including TF-IDF,word embedding,and self-attention mechanisms,are *** suggested model fares better than other models in terms of accuracy,obtaining a remarkable 96.54%,according to the trial *** study advances the discipline by presenting a new dataset and putting forth a practical model for Arabic dialect *** model may prove crucial for future work in sociolinguistic studies and NLP.

关键词： Dialectal Arabic transformers deep learning natural language processing systems

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Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square 2

Classification of Medicinal Wild Plants Using Radial Basis F...

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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Borman, Rohmat Indra Rossi, Farli Alamsyah, Dedy Nuraini, Rini Jusman, Yessi Fauziyah Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Muhammadiyah Tangerang Faculty of Engineering Tangerang Indonesia Universitas Nasional Jakarta Faculty of Communication Technology and Informatics Selatan Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia University of Science and Technology Beijing Faculty of Engineering Beijing China

ISBN: (纸本)9781665465410

Wild plants are considered as plants whose growth interferes with other plants. However, in its development it turns out that wild plants contain ingredients for medicines. However, information and knowledge about wild plant species, especially plants with herbaceous vegetation, needs to be carried out with various technologies, including through digital image processing. This study aims to develop a classification model for medicinal plant species using color and texture extraction using the Radial Basis Function Neural Network (RBFNN) algorithm with a combination of the Least Mean Square (LMS) algorithm. Color feature extraction is obtained by calculating the RGB average value and texture feature extraction using a Gabor filter obtained from the mean, entropy, and variance parameters. The result of feature extraction becomes input for RBFNN with LMS. RBFNN has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. The LMS algorithm is used for learning or updating neural network weights. Based on the test results by calculating precision, recall, and accuracy, it gets a precision value of 92.50%, recall gets a value of 91.74, accuracy gets a value of 92.08. These results indicate that the developed model can classify wild plant species with medicinal properties well. © 2022 IEEE.

关键词： Gabor filters

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Sorting Algorithm visualizer

Sorting Algorithm visualizer

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Thakkar, Aditya Kavita Dash, Sonali Joshi, Sanjeev Kumar Department of Computer Science Chandigarh University Punjab Gharuan India Faculty of Computer Science and Engineering Sgt University Gurugram122505 India Uttaranchal University Dehradun India

ISBN: (纸本)9781665461221

Visual learning is one of the most effective ways to grasp the knowledge of anything. Algorithm visualization demonstrates operation of logic in a pictorial manner, this enhances the knowledge and simplifies the understanding. In this paper, We address the need for a pictorial explanation of algorithms and suggest solution to visualize sorting algorithms by building a web application and explore probable future directions based on our own findings and observations using objective checklist of the web application. © 2022 IEEE.

关键词： Web services

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Network-based analysis to uncover drug-induced adverse side-effects

Network-based analysis to uncover drug-induced adverse side-...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Funari, Alessio Paci, Paola Conte, Federica Sapienza University of Rome Department of Translational and Precision Medicine Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350337488

Despite the drug approval process consists of extremely rigorous clinical and preclinical studies, not all side effects are identified before its marketing, posing a significant risk to public health. Furthermore, considering the huge use of economic and human resources, in-silico predictive approaches for the identification of side effects are essential. In this study, we introduce a new method based on random walk with restart algorithm to delineate previously unidentified links between drugs and side effects, and we apply it on the drug-induced Asthma and long QT syndrome. We identified the genes potentially involved in the development of the analyzed side effect by comparing side-effect-related drugs with drugs not known to induce side effects. Analyzing the sets of genes most likely influenced by the perturbation of each individual drug, we observed that, on average, side-effect-related drugs perturb a higher percentage of genes involved in the development of side effects compared to side-effect-unrelated drugs. Based on this finding, we developed a classifier to explore all possible unknown associations between drugs and side effects. This method can be extended to the analysis of other side effects as well. © 2023 IEEE.

关键词： network medicine random walk side effect

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International Conference on Networking, Intelligent systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 ACM.

关键词： Object oriented programming

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Combining Extreme Learning Machine and Linear Discriminant Analysis for Optimized Apple Leaf Disease Classification

Combining Extreme Learning Machine and Linear Discriminant A...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Parjito Imam Ahmad Rohmat Indra Borman Allan D Alexander Yessi Jusman Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Faculty of Computer Science Universitas Bhayangkara Jakarta Raya South Jakarta Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

Apple trees are an agricultural commodity with high economic value that often face serious challenges due to various leaf diseases. Early detection and proper treatment are crucial to reducing economic losses and ensuring healthy plant growth. Manual methods for detecting apple leaf diseases tend to be time-consuming and require specialized expertise, making them difficult to apply on a large scale. This study aims to develop an automatic classification system based on image processing to detect apple leaf diseases by combining Extreme Learning Machine (ELM) and Linear Discriminant Analysis (LDA) algorithms. ELM offers high training speed with randomly assigned weights, while LDA performs dimensionality reduction to retain the most relevant features, thereby improving model performance. The study results indicate that the combination of ELM and LDA produces a model capable of classifying apple leaf diseases with an accuracy of 92.50%, an improvement of 6.25% compared to using ELM alone. This demonstrates that the model is highly effective in classifying apple leaf diseases.

关键词： Training Economics Analytical models Accuracy Extreme learning machines Manuals Linear discriminant analysis Classification algorithms Diseases Testing

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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