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Advancing Safety through IoT: Gas Detection and Automation systems 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Rupina, Tasmina Akter Barua, Pusmita Habib, Israt Binteh Mahmud, Tanjim Nahar, Lutfun Zahan, Nusrat Hossain, Mohammad Shahadat Andersson, Karl International Islamic University Chittagong Chattogram Bangladesh Rangamati Science and Technology University Rangamati Bangladesh University of Chittagong Chattogram Bangladesh Luleå University of Technology Luleå Sweden

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Cooking food is an everyday task where gas is a necessary resource and leaking of gas might cause economic, financial as well as physical damage to the household. the inspiration for this thesis is to reduce the accidents and damages caused by gas leakage and explosion accidents. Nowadays gas explosion is a common accident Bangladesh is facing against. the fundamental issue is pipeline leakage and people leaving the gas stove on for unnecessary amounts of time. Our paper works on a system where instant precautions are taken to prevent gas leakage will be detected instantly as well as necessary steps will automatically generate such as an exhaust fan will start to generate, the buzzer will instantly start to send the signal, LCD will display the amount of gas leaking. Our system will also cut the power circuit off to prevent an explosion due to leaked gas as well as send an alert message to the owner. To reduce unnecessary use of gas and sudden leakage, our system also monitors ongoing gas usage continuously besides humidity and temperature value to the Blynk app. In comparison to other sensors DHT22 and MQ2 gas sensors gives faster and better gas detection system than others. When compared with other sensors and systems our system gives better and faster results than other researched systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Liquid crystal displays

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A Systematic Review on Crop Leaf Disease Identification Using Machine Learning and Deep Learning Techniques

A Systematic Review on Crop Leaf Disease Identification Usin...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Sahil K. Shah Vidya Kumbhar T. P. Singh Symbiosis Institute of Geoinformatics Symbiosis International (Deemed University) Pune India

World Population is increasing drastically and is expected to be around 12.3 billion in 2100. With the increase in population, it becomes important to foster the basic needs of mankind suitably. To withstand larger food requirements, getting higher crop yields is important. However, challenges like crop diseases, environmental factors like soil type, soil moisture, nutrient contents in soil, climate change, etc., affect various crop yields severely. the modern technologies can help in minimizing the adverse effects of these factors on crop yields. the advent of advanced automation techniques in Artificial Intelligence (AI) & Machine Learning (ML) can help in automated detection of crop diseases more accurately. However, use of such automated techniques face several challenges. Current study attempts to present a systematic literature review (SLR) of the existing body of knowledge in crop disease recognition & classification using automated techniques. the review followed PRISMA guidelines & searched four major databases with a comprehensive search query. 60 relevant research studies were identified after filtering through systematic inclusion & exclusion criteria for further analysis. Each research article is examined for the crop species used, the dataset used, & the methodology used. In each of the studied research papers, we looked at the performance measures utilized to assess the overall acquired findings for crop disease diagnosis & classification. It is observed that such systems have great potential to improve food security & agricultural productivity. However, more research is needed to overcome the challenges & limitations of the current methods and to ensure their validity & reliability in different settings and platforms. the readers of this study could acquire challenges faced by researchers in automated crop disease identification systems & their potential solutions to improve the performance of advanced algorithms.

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Model Based Design in Automotive Open System Architecture

Model Based Design in Automotive Open System Architecture

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international conference on ent computing and Control systems (ICICCS)

作者： Bhavesh Raju Mudhivarthi Vaibhav Saini Ayush Dodia Pritesh Shah Ravi Sekhar Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed) University Pune India Symbiosis Institute of Technology (SIT) Pune Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

Model based design (MBD) methodology is gaining popularity over all the world. Original equipment manufacturers (OEMs) as well as their suppliers are finding MBD more useful and effective over the traditional design methodologies. MBD is a system model-based designing process which primarily involves four concepts that make it highly efficient: model-based specification, simulation-based design, code generation-based implementation, and continuous testing and verification. the automotive open system architecture (AUTOSAR) provides a seamless grade of interfacing between the basic hardware functions of an automotive vehicle and the corresponding application software. AUTOSAR also handles the electronic control unit software-hardware architecture and integration for all automotive components. the application of MBD principles in AUTOSAR development provides a lot of benefits like shortened development time, design reusability, effective design and model maintenance/upgradation and overall improved product quality. this paper describes the AUTOSAR layered architecture with an MBD perspective, including its basic software architecture (BSW), runtime environment (RTE) and application layer, as well as popular workflows such as the top-down, bottom-up, and round-trip configurations.

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An automated computer vision system for extraction of retail food product metadata 1st

An automated computer vision system for extraction of retail...

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1st international conference on Artificial Intelligence and Cognitive computing, AICC 2018

作者： Gundimeda, Venugopal Murali, Ratan S. Joseph, Rajkumar Naresh Babu, N.T. GTO-CDS-LAB Cognizant Technology Solutions 7th Floor Building 20 Raheja Mindspace Hi-Tech City Road Hyderabad500081 India GTO-CDS-LAB Cognizant Technology Solutions SEZ Ave Elcot Sez Sholinganallur ChennaiTamil Nadu600119 India

ISBN: (纸本)9789811315794

With the rapid growth in retail e-commerce industry, most of the traditional in-store retailers are focusing more on online and mobile channels. To stay competitive, retailers need quality metadata and powerful search platforms that entice customers make effective buy decisions. Many retailers have incomplete and inaccurate product information on their Web sites, and they use multiple manual-intensive methods for acquiring product information from suppliers and third-party sources. there is no one proven channel through which retailers can achieve high-quality metadata. Our study proposes an automation method to improve the extraction of unstructured product metadata from food product label images using computer vision (CV), machine learning (ML), optical character recognition (OCR), and natural language processing (NLP). We propose an automatic image quality classification system to identify images that give a high degree of metadata extraction accuracy, and we propose a technique to improve the quality of images using traditional computer vision algorithms to improve text detection and OCR- and NLP-based metadata extraction accuracy. Our results show 95% accuracy for attribute extraction from high-quality product images with machine-printed characters having contrasting backgrounds. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Machine learning

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Research on Object Self-Organizing Iterative Cluster Algorithm of Emitter Identification

Research on Object Self-Organizing Iterative Cluster Algorit...

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international conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Xiaoxuan Wang Lianwang Diao Xin Xu Sci. & Technol. on Inf. Syst. Eng. Lab. China Electronics Technology Group Corp 28th Research Institute Nanjing Jiangsu CN

ISBN: (纸本)9781479986477

A novel sorting algorithm based on clustering is proposed to resolve problems such as low sorting precision and even failure which are caused by conventional radar signal sorting algorithm applied in present high-density signal environment. According to the characters of electromagnetic parameters, the advantages and disadvantages of the traditional clustering algorithms in data mining have been discussed in this paper. By using traditional clustering algorithm, users have to specify in advance how many clusters are being sought and it cannot be processed well if signal samples are not in spherical cluster distribution. An improved clustering algorithm based on self-organizing iterative analytic is presented in this paper, which avoid relying on prior knowledge or depending on signals' distribution. Simulation result proves the validity and practicability of this algorithm. It provides a new way for object clustering problem in emitter identification.

关键词： data mining iterative methods pattern clustering radar computing radar signal processing sorting electromagnetic parameter characters emitter identification low sorting precision object self-organizing iterative cluster algorithm radar signal sorting algorithm Clustering Analysis Clustering Evaluation Emitter Identification K-means data mining Cluster Analysis radar computing Clustering algorithms Radar signal processing iterative methods sorting programs grade Sorting algorithm RADAR SIGNALS emitters

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Cobotics for Improving Industry 5.0 Performance in Smart Manufacturing through the Lens of Resource Based View

Cobotics for Improving Industry 5.0 Performance in Smart Man...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Vijayakumar Bharathi. S Dhanya Pramod Kanchan Patil Symbiosis Centre for Information Technology Symbiosis International (Deemed University) Pune India

Industry 5.0 is emerging and the primary focus is human centricity with experimentation on human and machines collaboration. this study aims to identify critical factors of smart manufacturing systems performance for Industry 5.0. Based on Resource based view framework and Dynamic capability view theory, determinants of cobotics collaboration and work flow integration were recognized that were Human-Cobot relational competence, Industry 5.0 technology competence, and support from top management. Model for smart manufacturing system performance was empirically tested using responses of 302 employees from manufacturing industries in India. the study highlights that Industry 5.0 technology competence and support of top management were positively influencing cobotics collaboration and work flow integration. However Human-Cobot relational competence was found insignificant. the results also showed that cobotic collaboration had positive influence, whereas cobotic work flow integration had no influence on smart manufacturing system performance. the study provides practical implications to manufacturing industries and policy makers for industry 5.0.

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CESMP: Chinese-English Segment-aligned Multi-field Patent Data

CESMP: Chinese-English Segment-aligned Multi-field Patent Da...

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IEEE international conference on Cloud computing and Intelligence systems (CCIS)

作者： Wuying Liu Lin Wang Fumao Hu Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China School of Foreign Languages Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9781665441506

Patent data from various countries in the world implies the essence of scientific discovery and technological innovation of all human beings, but language differences have become a huge obstacle to patent data retrieval and communication. We hope to build a bridge from Chinese to English in the patent domain, so that English speakers can make better use of Chinese patent data. With the help of natural language processing technologies such as optical character recognition, Chinese text processing, machine translation and English text processing, we construct digital Chinese-English segment-aligned multi-field patent (CESMP) data from scanned Chinese patents. the current CESMP data consists of 610,310 patent documents in XML format. Each patent document contains six required fields (date, publication, ipc, title, abstract, and claim) and four optional fields (cpc, wipo, originalapplicant, and currentowner), among which the wipo, title, abstract, and claim fields are aligned with Chinese and English segments. Supported by well-structured bilingual patent data, on the one hand, the resource construction algorithms can efficiently build a bilingual patent dictionary and a parallel patent segment bank; on the other hand, the deep natural language processing algorithms can be effectively implemented into many practical intelligent applications such as cross-language patent retrieval, patent spam filtering, patent network analysis, patent machine translation, etc.

关键词： Optical filters Patents Technological innovation XML Network analyzers Adaptive optics Optical character recognition software

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Cost Optimisation on Cloud by Adoption of Rust Programming

Cost Optimisation on Cloud by Adoption of Rust Programming

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international conference on ent computing and Control systems (ICICCS)

作者： Yogesh Parab Samaya Pillai Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International (Deemed University) India

“Cloud computing” or “Compute over the cloud” is the on-demand delivery of IT resources like Computational power, networking infrastructure and various types of storage over the internet, which is scalable and elastic on demand. One only pays for the amount he uses. the traditional approach is to invest in on-premise hardware, buying, provisioning, and maintaining the same. Instead of the huge capital expenditure, operational expense on the cloud can be considered. One can save on costs, as maintenance and provisioning cost is transferred to the hyperscalers. Almost every other organization in the industry is adopting cloud computing as the go-to option. the flexibility and convenience are immense. Cloud is agile: Hyperscalers like AWS, Azure & GCP innovate continuously to bring about technological disruptions and support every reinvent in the techno space. this aids the developer communities worldwide not being bound by resource scarcity. Complex apps can be deployed in minutes and can be accessed from every part of the world. Developers gain the freedom to experiment with every possible technology, test new plans, and essentially transform and move towards a better future. With the increased popularity of Cloud computing, multiple Tech giants have jumped into this space to provide cloud services and hence generate revenue. With the availability of a gamut of services and resources, multiple pricing tiers and a multitude of minuscule decisions at every step of implementation, cost optimization becomes a very crucial aspect to focus on. Every decision affects the cost one pays, from designing, resource provisioning and deployment. Hence this study tries to address various methods of cost optimization on the cloud. the narrative literature review approach has been adopted for the conceptualization of the model. the study provides implications to practitioners and developers for designing and developing cloud solutions at par with their requirements with optimal prici

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Wideband Conformal Meandered Bowtie Antenna for Capsule Endoscopy systems

Wideband Conformal Meandered Bowtie Antenna for Capsule Endo...

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international Electromagnetic Compatibility conference (EMC Turkiye)

作者： Amir Arayeshnia Majid Rafiee Mohammad Alibakhshikenari Chan Hwang See Ernesto Limiti Faculty of Technical and Engineering Imam Khomeini International University Qazvin Iran Department of Electrical Engineering Ooj Institute of Higher Education Qazvin Iran Department of Signal Theory and Communications Universidad Carlos III de Madrid Madrid Spain School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh Scotland Electronic Engineering Department University of Rome “Tor Vergata” Rome Italy

this article presents a wideband conformal meandered bowtie antenna for capsule endoscopy systems for gastrointestinal tract monitoring. the antenna has an operational bandwidth of 0.39-1.06 GHz (92%) and can operate in the frequency band of medical implant communication systems (i.e. 401-406MHz), as well as ISM bands (433.05-434.79 MHz and 902-928MHz). A biocompatible superstrate is also considered to prevent direct contact between radiating elements and biological tissues. the radiation efficiency of the proposed antenna exceeds 90% over the operational spectrum. the proposed antenna also has a stable omnidirectional radiation pattern, enabling a reliable wireless communication connection during the monitoring process.

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Enhancing Low-Resource Bangla Fake News Detection through Deep Convolutional Neural Networks 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Habiba, Sultana Umme Johora, Fatema Tuj Mahmud, Tanjim Tasnim, Tanpia Tasnim, Farzana Kabir, Sumaiya Hossain, Mohammad Shahadat Andersson, Karl Khulna University of Engineering and Technology Khulna Bangladesh Green University of Bangladesh Dhaka Bangladesh Rangamati Science and Technology University Rangamati Bangladesh International Islamic University Chittagong Chittagong Bangladesh University Chittagong Chittagong Bangladesh Luleå University of Technology Lulea Sweden

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

In today’s digital landscape, the detection of false information is of utmost importance, especially in languages like Bangla, which lack abundant natural language processing (NLP) resources. the rapid spread of misinformation through online platforms, particularly within Bangla-speaking communities, has become a pressing concern. However, the limited availability of NLP tools for Bangla has posed significant challenges in developing reliable models for identifying deceptive content. In response to these challenges, researchers have made notable progress in classifying Bangla news using deep learning techniques and language models like BERT. this study presents a detailed exploration of a deep convolutional neural network (CNN) model tailored for categorizing Bangla news articles as authentic or counterfeit. By integrating BERT (Bangla Electra) into the model’s architecture, an impressive accuracy rate of 94.33% was achieved, with our proprietary model surpassing this at 94.5%.To ensure the reliability of results, a range of NLP techniques were applied during data preprocessing, including data cleansing, tokenization, stop word and punctuation removal, and stemming. Feature extraction involved the combined use of TF-IDF and Bag of Words techniques. the dataset, obtained from Kaggle, comprised 7,000 genuine news texts and 1,000 counterfeit news texts. In summary, this research significantly contributes to Bangla news classification by showcasing the effectiveness of deep CNN models in accurately discerning between legitimate and fabricated news articles. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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