000			03983nas a2200409 a 4500
005			20260817161747.0
008			260616s20269999th u ms t 000 eng d
035			_a.b12507891
099		9	_aAIT Thesis no.EV-26-06
100	1		_aLaraib
245	1	0	_aAdvancing in-situ microplastic detection using electrical sensing zone empowered by machine learning and deep learning approaches
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2026
300			_a118 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. EV-26-06
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management
502			_aThesis (M. Sc.) - Asian Institute of Technology, 2026
520			_aThe problem of pollution with microplastic debris has become one of the most pressing ones because of the resistance of plastics to biodegradation and existing challenges of the classical approach to their rapid detection in aquatic ecosystems. This work studied the potential for applying the method of Electrical Sensing Zone (ESZ) combined with machine learning and deep learning techniques for classifying particles. The chosen classes include three types of microplastics: polyethylene terephthalate (PET), polypropylene (PP), and polystyrene (PS); two classes of inorganic particles: silicon dioxide (SiO₂) and calcium carbonate (CaCO₃); and Chlorella algae as a class of biological particles.The raw signals have been generated under laboratory conditions as voltage-time waveforms. Further preprocessing comprised denoising, baseline correction, peak detection, and pulse extraction. The signal of Chlorella algae particles was close to the baseline. Thus, the main classification analysis involved microplastic particles and inorganic particles' signals. Pulse signals have been encoded both as engineered feature vectors for feature-based machine learning and as fixed-length segments (windows) for CNN classification. The strategies considered included the following: five-class machine learning, binary machine learning, binary CNN classification, flat three-particle CNN classification, step-wise hierarchical three-particle CNN classification, and PET/PS/PP subtype CNN classification.The performance of five-class feature-based machine learning models proved low since the best results were obtained by Random Forest with a baseline test accuracy of 44.13%, followed by Boosting (42.76%) and SVM (38.43%). For the binary machine learning classifier, which could classify microplastics against inorganic particles, the performance was not taken into account here. CNN models demonstrated stronger classification capabilities: binary CNN yielded 91.98% of test accuracy; flat three-particle CNN {u2013} 58.13%; step-wise hierarchical three-particle CNN {u2013} 74.47%; PET/PS/PP subtypes CNN {u2013} 51.05%.
650		0	_aCoulter principle
650		0	_aMicroplastics _xDection _xTechnique
650		0	_aMachine learning
700	1		_aXue, Wenchao, _eChairperson
700	1		_aGhimire, Anish, _eExamination Committee
700	0		_aAttaphongse Taparugssanagorn, _eExamination Committee
710	2		_aPMU-KPCIP-AIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. EV-26-06
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B24442
907			_a.b12507891 _bmnarc _ca
902			_a260622
998			_b0 _c260617 _dm _eh _fa _g0
945			_lmnarc
945			_lmnarc
942			_c67
942			_c40
909			_aBarcode : - _bCREATED : 2026-06-16 _cRECORD # : i13605148 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050120421044 _bCREATED : 2026-06-17 _cRECORD # : i13605872 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c2564 _d2564