000			08075nam a2201225Ia 4500
000			05927ntm a2200205 i 4500
001			91281
003			0
005			20250920173824.0
008			241029n 000 0 eng d
010			_z _z _o _a _b
015			_22 _a
016			_2 _2 _a _z
020			_e _e _a _b _z _c _q _x
022			_y _y _l _a2
024			_2 _2 _d _c _a _q
028			_a _a _b
029			_a _a _b
032			_a _a _b
035			_a _a _b _z _c _q
037			_n _n _c _a _b
040			_e _erda _a _d _b _c
041			_e _e _a _b _g _h _r
043			_a _a _b
045			_b _b _a
050			_a _a _d _b2 _c0
051			_c _c _a _b
055			_a _a _b
060			_a _a _b
070			_a _a _b
072			_2 _2 _d _a _x
082			_a _a _d _b2 _c
084			_2 _2 _a
086			_2 _2 _a
090			_a _a _m _b _q
092			_f _f _a _b
096			_a _a _b
097			_a _a _b
100			_e _e _aCayco, Francis Mark N. Marquez, Joshua Marc D. Mercullio, Miraiza Elisa V. Paclian, Micha Lene R. Villar, Helen Claire J. _d _b4 _u _c0 _q16
110			_e _e _a _d _b _n _c _k
111			_a _a _d _b _n _c
130			_s _s _a _p _f _l _k
210			_a _a _b
222			_a _a _b
240			_s _s _a _m _g _n _f _l _o _p _k
245		0	_a _aIntegratedn mosquito detection and fumigation system utilizing faster region-based convolutional neural networks. _d _b _n _c _h6 _p
246			_a _a _b _n _i _f6 _p
249			_i _i _a
250			_6 _6 _a _b
260			_e _e _a _b _f _c _g
264			_3 _3 _a _d _b _c4544446
300			_e _e _c28 cm. _axviii, 256 pp. _b
310			_a _a _b
321			_a _a _b
336			_b _atext _2rdacontent
337			_3 _30 _b _aunmediated _2rdamedia
338			_3 _30 _b _avolume _2rdacarrier
340			_2 _20 _g _n
344			_2 _2 _a0 _b
347			_2 _2 _a0
362			_a _a _b
385			_m _m _a2
410			_t _t _b _a _v
440			_p _p _a _x _v
490			_a _a _x _v
500			_a _aUndergraduate Thesis: (Bachelor of Science in Computer Engineering) - Pamantasan ng Lungsod ng Maynila, 2024. _d _b _c56
504			_a _a _x
505			_a _a _b _t _g _r
506			_a _a5
510			_a _a _x
520			_b _b _c _aABSTRACT: STATEMENT OF THE PROBLEM: Mosquitos, carriers of diseases like malaria, dengue fever, Zika virus, and West Nile virus, pose a global public health threat, particularly in endemic regions. These diseases can lead to serve illness, death, and economic burdens. Conventional mosquito control methods often use pesticides, which have limitations in precision and can harm non-target species while fostering pesticide resistance in mosquitos. To address these challenges, a more precise and targeted approach is required. Urbanization is increasing the health threat by mosquito-borne diseases. This growth creates more potential breeding sites for mosquitos. The current mosquito detection and fumigation systems suffer from a critical lack of integration and comprehensive analytics. While some fumigation systems are in place, they operate independently without offering insights into the extent of mosquito infestation or the effectiveness of fumigation efforts. RESEARCH METHODOLOGY: This research adopts a quantitative approach, integrating computer science, internet of things, and web development to create an automated mosquito control system. The system features a multi-domain architecture for image-based mosquito detection and targeted fumigation. It includes: 1. Detection Domain - Processes images using an ESP-32 Camera and uploads the photo to the backend server for machine learning processing. Also contains the luring mechanism designed to attract mosquitos to the detection platform. 2. Fumigation Domain - A subsystem controlled by the ESP-32 Wi-Fi controlled by the spraying system. Automated fumigation is triggered via a 12-V Relay Module. 3. Machine Learning Domain - Contains the Faster RCNN Model where datasets are obtained and augmented; and model is trained and tested. 4. Website Domain - A publicly-available website that allows stakeholders to view mosquito-related data. User interaction is facilitated through a web interface. SUMMARY FINDINGS: The detection system's neural network showed significant improvement during the training phase, with metrics like Region Proposal Regression and Class Regression Loss enhancing region and bounding box accuracy. The model demonstrated high precision and recall for larger objects, through ir struggled with smaller ones, and required fine-tuning for crowded scences. The luring system, tested with acetic acid, UV light, and heat pads, revealed acetic acid as a superior lure, effectively drawing mosquitos compared to dry ice. UV light's effectiveness varied with environmental conditions, while heat pads consistently attracted mosquitos by simulating body heat. Rge automated fumigation system, controlled by a microcontroller and ESP32 module, efficiently used a 12-V Relay Module and DC micro diaphragm pump to spray pesticide, with each 5L tank lasting up to 18 fumigations and reaching 1.5-2 meters. The real-time data analytics website dashboard performed well, meeting metrics for load time, responsiveness, and data visualization. It integrated multiple detection and fumigation systems for comprehensive data analysis but faced hardware limitations preventing direct system interaction. User satisfaction survey showed that respondents had a positive impression, believing the system could mitigate negative effects and reduce disease spread. CONCLUSION: The interdisciplinary approach adopted in this study, combining computer science, machine learning, and web development, holds promise for transforming mosquito control efforts in the barangay community of Tondo, Manila, and beyond. By leveraging technology to mitigate the threat of mosquito-borne diseases, we move closer to a future where communities are safer, healthier, and more resilient in the face of emerging public health challenges. RECOMMENDATIONS: This project demonstrated the potential of a multi-domain architecture for automated mosquito control, effectively using image capture, machine learning-based detection and counting, and targeted fumigation. To optimize the systems's efficacy, user experience, and environmental impact, several advancements are recommended. First, leveraging faster GPU's to reduce training time and exploring alternative neural network models like YOLOv4 enhance performance. For luring mechanisms, testing different attractant chemicals and rigorously evaluating equipment effectiveness beyond integration testing are crucial. In terms of fumigation, using less pervasive chemicals for safety, expanding spray coverage, and increasing chemical container capacity will improve operational duration and safety. Enhancing the website by reducing latency, integrating faster APIs, and adding visual aids like graphs and charts will improve the user experience. To optimize energy efficiency and scalability, minimizing wires and cords, expanding system coverage, and incorporating solar panels and batteries for a more independent and sustainable prototype are essential. _u
521			_a _a _b
533			_e _e _a _d _b _n _c
540			_c _c _a5
542			_g _g _f
546			_a _a _b
583			_5 _5 _k _c _a _b
590			_a _a _b
600			_b _b _v _t _c2 _q _a _x0 _z _d _y
610			_b _b _v _t2 _x _a _k0 _p _z _d6 _y
611			_a _a _d _n2 _c0 _v
630			_x _x _a _d _p20 _v
648			_2 _2 _a
650			_x _x _a _d _b _z _y20 _v
651			_x _x _a _y20 _v _z
655			_0 _0 _a _y2 _z
700			_i _i _t _c _b _s1 _q _f _k40 _p _d _e _a _l _n6
710			_b _b _t _c _e _f _k40 _p _d5 _l _n6 _a
711			_a _a _d _b _n _t _c
730			_s _s _a _d _n _p _f _l _k
740			_e _e _a _d _b _n _c6
753			_c _c _a
767			_t _t _w
770			_t _t _w _x
773			_a _a _d _g _m _t _b _v _i _p
775			_t _t _w _x
776			_s _s _a _d _b _z _i _t _x _h _c _w
780			_x _x _a _g _t _w
785			_t _t _w _a _x
787			_x _x _d _g _i _t _w
800			_a _a _d _l _f _t0 _q _v
810			_a _a _b _f _t _q _v
830			_x _x _a _p _n _l0 _v
942			_a _alcc _cBK
999			_c25816 _d25816