Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

被引:14
|
作者
Malik, Meena [1 ]
Prabha, Chander [2 ]
Soni, Punit [2 ]
Arya, Varsha [3 ,4 ]
Alhalabi, Wadee Alhalabi [5 ]
Gupta, Brij B. [6 ,7 ,8 ,9 ]
Albeshri, Aiiad A. [10 ]
Almomani, Ammar [7 ,11 ]
机构
[1] Chandigarh Univ, Dept CSE, Mohali, India
[2] Chitkara Univ, Inst Engn & Technol, Rajpura, India
[3] Asia Univ, Dept Business Adm, Taichung, Taiwan
[4] Chandigarh Univ, Chandigarh, India
[5] King Abdulaziz Univ, Dept Comp Sci, Immers Virtual Real Res Grp, Jeddah, Saudi Arabia
[6] Asia Univ, Dept Comp Sci & Informat Engn, Taichung, Taiwan
[7] Skyline Univ Coll, Sch Comp, Sharjah, U Arab Emirates
[8] Lebanese Amer Univ, Beirut, Lebanon
[9] Univ Petr & Energy Studies UPES, Ctr Interdisciplinary Res, Dehra Dun, Uttarakhand, India
[10] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Comp Sci, Jeddah, Saudi Arabia
[11] Al Balqa Appl Univ, Salt, Jordan
关键词
Litter Detection and Classification; Machine Learning; Neural Networks; Smart City; MATURITY MODEL;
D O I
10.4018/IJSWIS.324105
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] Machine Learning-Based Detection of Ransomware Using SDN
    Cusack, Greg
    Michel, Oliver
    Keller, Eric
    PROCEEDINGS OF THE 2018 ACM INTERNATIONAL WORKSHOP ON SECURITY IN SOFTWARE DEFINED NETWORKS & NETWORK FUNCTION VIRTUALIZATION (SDN-NFVSEC'18), 2018, : 1 - 6
  • [42] Machine Learning Based Security for Smart Cities
    Amaizu, Gabriel Chukwunonso
    Lee, Jae-Min
    Kim, Dong-Seong
    2022 27TH ASIA PACIFIC CONFERENCE ON COMMUNICATIONS (APCC 2022): CREATING INNOVATIVE COMMUNICATION TECHNOLOGIES FOR POST-PANDEMIC ERA, 2022, : 572 - 573
  • [43] A Survey of Smart Home IoT Device Classification Using Machine Learning-Based Network Traffic Analysis
    Jmila, Houda
    Blanc, Gregory
    Shahid, Mustafizur R.
    Lazrag, Marwan
    IEEE ACCESS, 2022, 10 : 97117 - 97141
  • [44] Teat detection mechanism using machine learning based vision for smart Automatic Milking Systems
    Rastogi, Akanksha
    Pal, Abhishesh
    Joung, Kim Man
    Ryuh, Beom Sahng
    2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI), 2017, : 947 - 949
  • [45] Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment
    Alsubaei, Faisal S.
    Al-Wesabi, Fahd N.
    Hilal, Anwer Mustafa
    APPLIED SCIENCES-BASEL, 2022, 12 (05):
  • [46] Machine Learning-Based Automatic Classification of Knee Osteoarthritis Severity Using Gait Data and Radiographic Images
    Bin Kwon, Soon
    Han, Hyuk-Soo
    Lee, Myung Chul
    Kim, Hee Chan
    Ku, Yunseo
    Ro, Du Hyun
    IEEE ACCESS, 2020, 8 : 120597 - 120603
  • [47] Detection of Lung Diseases Using Deep Transfer Learning-Based Convolution Neural Networks
    Prakash, Ankur
    Singh, Vibhav Prakash
    ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2023, PT IV, 2024, 2093 : 82 - 92
  • [48] Machine Learning-Based Classification of Mushrooms Using a Smartphone Application
    Lee, Jae Joong
    Aime, M. Catherine
    Rajwa, Bartek
    Bae, Euiwon
    APPLIED SCIENCES-BASEL, 2022, 12 (22):
  • [49] Smart Meter Systems Detection & Classification using Artificial Neural Networks
    Bier, Thomas
    Abdeslam, Djaffar Ould
    Merckle, Jean
    Benyoucef, Dirk
    38TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2012), 2012, : 3324 - 3329
  • [50] Automatic Reclaimed Wafer Classification Using Deep Learning Neural Networks
    Shih, Po-Chou
    Hsu, Chun-Chin
    Tien, Fang-Chih
    SYMMETRY-BASEL, 2020, 12 (05):