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Relevant bibliographies by topics / Intelligent waste segregation

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Academic literature on the topic 'Intelligent waste segregation'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Intelligent waste segregation"

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Afolabi, Olaitan O. "Leveraging Intelligent Waste Segregation System for Renewable Energy and Environmental Sustainability." FUOYE Journal of Engineering and Technology 9, no. 3 (2024): 447–51. https://doi.org/10.4314/fuoyejet.v9i3.11.

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The continuous increase in human population across the world’s continents has given rise to the volume of waste generated. Improper waste management poses environmental and health risk, as such it is paramount to take meticulous measures to manage waste. Waste segregation facilitates the collection of recyclable materials and the safe disposal of non-recyclable ones. The manual method of waste segregation is unhygienic, costly, and time-consuming. This work proposes an Intelligent Waste Segregation System to cater for these setbacks. The proposed system integrates the working principle of indu

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Ramani Bai Gopinath, V., S. H. Dhavarpanah, G. Kangadharan, and R. Ruzaimah. "Fuzzy Logic Intelligent system for an Automatic medical waste segregation." Journal of Physics: Conference Series 2040, no. 1 (2021): 012004. http://dx.doi.org/10.1088/1742-6596/2040/1/012004.

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Abstract AUTOM is an acronym given to a commercially designed automatic waste disposal master tool for medical laboratories and clinical waste separation through this research study. In this research, a fuzzy rule-based system is designed which can segregate 24 different waste types which are selected from 8 more common medical waste groups. In the proposed system, after capturing each frame some pre-processing operations are done, features are extracted, fuzzy parameters, fuzzy terms and fuzzy rules are determined and finally a rule with a maximum certification degree is fired. In the designe

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bhiungade, Prof V. B., Abhay Londhe, Vishal Patil, and Devesh Shinde. "Waste Segregation system." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04708.

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Effective waste management is a critical challenge in modern urban environments due to the exponential growth in population, consumption, and industrial activity. This project report presents the design, development, and implementation of an automated Waste Segregation System aimed at improving efficiency in waste disposal and recycling processes. The proposed system integrates mechanical and electronic components, including sensors, microcontrollers (such as Arduino or Raspberry Pi), and conveyance systems to automatically segregate waste into biodegradable, non-biodegradable, and recyclable

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Kanawade, Swarangi. "AI Recycle Bin: Revolutionizing Waste Management Through AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41389.

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This paper presents the AI Recycle Bin, an intelligent waste segregation system that leverages artificial intelligence (AI) classifiers and sensor technology for automated waste management. The system integrates machine learning algorithms and sensors to classify waste into categories such as plastic, metal, paper, and organic material. By combining image recognition and real-time sensor input, the AI Recycle Bin ensures efficient recycling, reduces human effort, and promotes environmental sustainability. The solution supports smart city initiatives and contributes to sustainable development.

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M., Thangavel, Chitirap Paavai L., Jayasuriya P J., Thalir Swathi B., Dinesh D., and PraveenaSri R. "Waste Classification System using Smart Sensors." June 2024 6, no. 2 (2024): 163–73. http://dx.doi.org/10.36548/jsws.2024.2.007.

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An innovative approach to revolutionize waste management through the integration of advanced sensor technology and intelligent algorithms is proposed. Effective waste segregation is crucial in addressing the current needs. The main objective is to develop an efficient waste segregation system using various sensors to segregate degradable, non-recyclable, bio-medical waste, and organic-waste. A MCU unit with various types of sensors is used for segregation. A DC motor with conveyor belt is used to transfer wastes to respective bins, while an I2C connector with an LCD display is used to indicate

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Patankar, Yukti, Manasi Mirge, Vaishnavi Edakhe, Pooja Gohokar, Ruchika Malpe, and Prajakta Singam. "Real-Time Waste Object Segregation Using Convolutional Neural Network." International Journal of Computer Science and Mobile Computing 11, no. 2 (2022): 85–88. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.010.

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The increasing number in solid waste in the urban area is becoming a great concern, and it would result in environmental pollution and may be dangerous to human health if it is not properly organized It is important to have an advanced or intelligent waste management system to manage a variety of waste material. One of the most important steps of waste management is the separation of the waste into the different components and this process is normally done by handpicking. To simplify the process, we propose an intelligent waste material classification system, which contains 14 million images b

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Rahmawati, Siti Solehah Yunita, Desy Khalida Maharani, and Wina Munada. "Intelligent Waste Segregation System Using Convolutional Neural Networks for Deep Learning Applications." IC-ITECHS 5, no. 1 (2024): 500–511. https://doi.org/10.32664/ic-itechs.v5i1.1579.

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Efficient waste management is essential for environmental sustainability and reducing landfill burdens. This study proposes an Intelligent Waste Segregation System leveraging Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to automate the classification of waste into recyclable and non-recyclable categories. The purpose of this research is to enhance waste sorting accuracy and efficiency using advanced deep learning techniques. The system employs VGG-16, pre-trained on a large dataset, and fine-tuned with a waste image dataset, enabling high precision in recognizing waste

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Alourani, Abdullah, M. Usman Ashraf, and Mohammed Aloraini. "Smart waste management and classification system using advanced IoT and AI technologies." PeerJ Computer Science 11 (April 1, 2025): e2777. https://doi.org/10.7717/peerj-cs.2777.

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The effective management of municipal solid waste is a critical global issue, affecting both urban and rural areas. To address the growing volume of solid waste, proactive planning is essential. Traditionally, solid waste is often disposed of without segregation, preventing recycling and the recovery of raw materials. Proper waste segregation is a fundamental requirement for effective solid waste management, allowing materials to be recycled efficiently. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) offer powerful tools for

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Shejawadakar, Someshwar J., Ganesh D. Daivgnya, Raghavendra V. Mujumdar, Kirankumar Patil, and Karthik A S. "“Intelligent pantry waste collection and segregation in Indian railway coach”." International Journal of Futures Research And Development 01, no. 01 (2020): 27–37. http://dx.doi.org/10.46625/ijfrd.2020.1104.

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Sinduja, B., and Tarun Kumar. "An Intelligent App-based System for Waste Segregation and Collection." Procedia Computer Science 235 (2024): 2843–56. http://dx.doi.org/10.1016/j.procs.2024.04.269.

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Book chapters on the topic "Intelligent waste segregation"

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Verma, Rahul Kumar, and Suneeta Agarwal. "Waste Segregation to Ease Recyclability." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1295-4_25.

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Sambooranalaxmi, S., P. Karuppasamy, M. Muthukarthika, C. Petchimuthu, and G. Saranya. "Embedded C-Based Automatic Waste Segregation System." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3608-3_30.

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Sarobin, Vergin Raja, Hetal Atwal, Varsha Sharma, and Agrim Sharma. "Deepening Sustainability: Waste Segregation Through Advanced Deep Learning Techniques." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3191-6_11.

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Habib, Fakih Awab, Khan Salman Mehtabali, Khan Athar, and Ansari Mohd Afwan. "Automatic Segregation and Supervision of Waste Material Using Industrial Control Devices." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6707-0_38.

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Bawankule, Ram, Vaishnavi Gaikwad, Indrayani Kulkarni, Shivam Kulkarni, and Archana Jadhav. "A Review Paper on Techniques for Collection and Segregation of Solid Waste." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3485-0_53.

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Kumar, C. Ashish, V. Kakulapati, G. Prasadu, Manish Mokurala, and Ganesh Nag Manoori. "An Intelligent Analysis of Waste Identification and Segregation Using Swarm Optimization." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1264-2_19.

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Srivastava, Payal, Vikas Deep, Naveen Garg, and Purushottam Sharma. "SWS—Smart Waste Segregator Using IoT Approach." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_53.

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Davesar, Parth, Aditi Bhati, Vijay Arora, Shobha Tyagi, and Pronika Chawla. "Smart dustbin for efficient waste segregation in IoT-enabled environments." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003510833-44.

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Singh, Drishti, E. Manoj, and T. Anjali. "Waste Segregator: An Optimized Neural Learning Approach Towards Real-Time Object Classification." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2597-8_49.

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Aditya Gowrish, Menti, Mukesh Kumar Dahlan, and R. Subhashini. "Intelligent Waste Classification System Using Vision Transformers." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210061.

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The issue of waste management is a growing concern, ranging from agricultural fields to industries and villages to cities. Our project aims to contribute to this issue by solving the problem of segregation of waste by using the latest advances in computer vision. The Transformers for Image Recognition at Scale, which will give highly efficient results in less time compared to the models based on Convolutional Neural Networks which loses a lot of valuable information and ignores the relationship between part of images and as a whole. The Self-Attention in vision transformers gives them the capa

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Conference papers on the topic "Intelligent waste segregation"

1

Kalaiarasi, A. R., and M. Prabakaran. "Design and Development of an Intelligent Automated Waste Segregation System Using Arduino for Efficient Waste Management." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847447.

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S, Premalatha, Sathies Kumar T, Sathya Priya M, Dhiksha S. V, Subhiksha V, and Reshika A. V. K. "Intelligent Waste Disposal: An Iot Enabled Automated System for Dumpster Monitoring and Medical Segregation." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780351.

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Cho, Hyuntae. "Transparent Plastic Bottle Detection and Depth Decision Method using YOLOv8 in Recyclable Waste Segregation Systems." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622125.

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Kittappa, Thiagarajan, T. Padmavathi, Kumuda P.R, G. B. Suresh, G. Murugesan, and Revathi V. "An Intelligent Internet of Things Based Health Care Hospital Management of Medical Waste and its Segregation." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961033.

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Vidhyalakshmi, P., K. Prabhu, M. Malathi, P. M. Lathika Sri, R. Manisha, and K. Kamalnath. "Automated Paper Waste Segregation System." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10761021.

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Gadagkar, Akhilraj V., Shifana Begum, and Rameesa K. "Sustainable Urban Waste Management: A Scalable IoT System for Automated Segregation and Real-Time Monitoring." In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE). IEEE, 2025. https://doi.org/10.1109/aide64228.2025.10987298.

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Gupta, Nimisha S., V. Deepthi, Maya Kunnath, Pal S. Rejeth, T. S. Badsha, and Binoy C. Nikhil. "Automatic Waste Segregation." In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663148.

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Nagajyothi, D., Shaik Ashik Ali, V. Jyothi, and Praharsha Chinthapalli. "Intelligent Waste Segregation Technique Using CNN." In 2023 2nd International Conference for Innovation in Technology (INOCON). IEEE, 2023. http://dx.doi.org/10.1109/inocon57975.2023.10101021.

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D, Bhuvana Suganthi, Priyanka S, Varsha A, and Karnika S. "Smart Waste Segregation System." In 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2024. http://dx.doi.org/10.1109/iitcee59897.2024.10467618.

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Jaswal, Pragun, Zubair Ahmad Sofi, Er Akshay Kanwar, and Vicky Kumar. "Intelligent Waste Segregation Using Smart IoT-based Dustbin." In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE). IEEE, 2023. http://dx.doi.org/10.1109/aece59614.2023.10428256.

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