Smart Waste Management and Classification System for Smart Cities using Deep Learning

Hasan, Mohammad Kamrul; Khan, Muhammad Adnan; Issa, Ghassan F.; Atta, Ayesha; Akram, Ali Sheraz; Hassan, Muhammad

doi:10.1109/icbats54253.2022.9759087

Cited by 62 publications

(7 citation statements)

References 18 publications

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“…Combining IoTs with deep learning provides a modular approach for data classification and real-time analysis. This article [74] depicts an effective smart waste management and classification system based on IoT and deep learning. The study proposes a microchip-based trash can with a fast waste collection system.…”

Section: Countermeasuresmentioning

confidence: 99%

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An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities

Demertzi

Demertzis

2023

Applied Sciences

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A smart city is where existing facilities and services are enhanced by digital technology to benefit people and companies. The most critical infrastructures in this city are interconnected. Increased data exchange across municipal domains aims to manage the essential assets, leading to more automation in city governance and optimization of the dynamic offered services. However, no clear guideline or standard exists for modeling these data flows. As a result, operators, municipalities, policymakers, manufacturers, solution providers, and vendors are forced to accept systems with limited scalability and varying needs. Nonetheless, it is critical to raise awareness about smart-city cybersecurity and implement suitable measures to safeguard citizens’ privacy and security because cyber threats seem to be well-organized, diverse, and sophisticated. This study aims to present an overview of cyber threats, attacks, and countermeasures on the primary domains of smart cities (smart government, smart mobility, smart environment, smart living, smart healthcare, smart economy, and smart people). It aims to present information extracted from the state of the art so policymakers can perceive the critical situation and simultaneously be a valuable resource for the scientific community. It also seeks to offer a structural reference model that may guide the architectural design and implementation of infrastructure upgrades linked to smart city networks.

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Section: Countermeasuresmentioning

confidence: 99%

“…In parallel, this study [73] suggests a cyber-security monitoring system that connects timeseries event data, visually [74] depicting security occurrences. It provides a predictive prediction of probable circumstances based on established situations.…”

Section: Countermeasuresmentioning

confidence: 99%

An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities

Demertzi

Demertzis

2023

Applied Sciences

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“…Deep neural networks [45] and Collaborative Networked Learning [46] can improve the accuracy of predictions by analyzing complex patterns in data. Mobile learning [47] systems, analogical reasoning [48], m-learning [49], ensemble learning [50], and Augmented Reality [51] and deep learning [52] can enhance the learning experience and support various applications, including accounts privacy and management, by providing personalized and engaging content.…”

Section: Interactive Kiosksmentioning

confidence: 99%

Applications in Home Improvement Retailer, Koctas

Mourad

Raza

Amjad

et al. 2023

2023 International Conference on Business Analytics for Technology and Security (ICBATS)

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It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like experience. Finally, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective marketing. Overall, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry.

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“…In this paper, we performed the data set acquisition, data preprocessing, convolution neural network structure design, super parameter selection and training model to get the training results. We connected a whole set of program sub modules to complete the deep learning part of the garbage classification program and to realize the target function.. Smart Waste Management and Classification System for Smart Cities using Deep Learning proposed by M. K. Hasan, M. A. Khan, G. F. Issa, A. Atta, A. S. Akram and M. Hassan, [7], For modern city environments to be renewable and clean, waste management and recycling are essential. Solid waste management, disposal, and recycling are issues in many Pakistani cities, particularly Karachi and Lahore.…”

Section: Introductionmentioning

confidence: 99%

Online Waste Management System

Babu¹

2023

IJRASET

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An online waste management system is a web-based platform designed to help manage waste collection, disposal, and recycling processes. This type of system can offer many benefits, including increased efficiency, reduced costs, and improved environmental sustainability. The system typically includes features such as scheduling waste pickups, tracking waste generation, monitoring waste disposal methods, and generating reports on waste management activities. This abstract provides an overview of online waste management systems, discussing their key features and benefits. It also explores the challenges associated with implementing such systems and suggests some best practices for achieving optimal outcomes. Overall, an online waste management system can help organizations reduce their environmental impact while improving their waste management processes.It Focuses on engaging customers and volunteers is a web-based platform designed to involve and motivate individuals to participate in waste management activities. This type of system can offer many benefits, including increased community engagement, improved recycling rates, and reduced waste generation.

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Smart Waste Management and Classification System for Smart Cities using Deep Learning

Cited by 62 publications

References 18 publications

An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities

An Overview of Cyber Threats, Attacks and Countermeasures on the Primary Domains of Smart Cities

Applications in Home Improvement Retailer, Koctas

Online Waste Management System

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