**6. Conclusions**

The IoT- and cloud-based waste management and recycling system we have implemented successfully addresses the pressing issue of e-waste. Our study focused on the efficient separation and quick disposal of e-waste using the IoT, cloud computing, and machine learning. Our research results showcased numerous advantages, including enhanced efficiency, cost reductions, improved monitoring capabilities, and increased sustainability. Real-time data collection and analysis facilitated optimized waste collection routes, minimized the environmental impact, and successfully produced bio-fuel and solar batteries. Our research objectives were achieved through the implementation and evaluation of an IoT- and cloud-based waste management system, resulting in improved waste monitoring, optimized collection routes, and turning waste into assets by producing bio-fuel through pyrolysis and converting e-waste metal into solar batteries. Our study's outcomes align seamlessly with our initial research objectives, demonstrating the system's ability to overcome challenges associated with traditional waste management practices. However, there are some limitations, such as security and privacy concerns related to IoT devices and cloud infrastructure that must be addressed with robust measures to ensure data protection. In addition, the performance of the GAN algorithm can be affected by issues such as mode collapse, where the generator produces limited varieties of output, instability during training, and difficulty in evaluating the generated images. To sum up, the implementation of our IoT- and cloud-based waste management system has immense potential to revolutionize waste management practices. Its real-time data gathering, operational optimization, resource allocation, and production of recycled products offer substantial cost savings, a reduced environmental impact, and improved sustainability. However, addressing security concerns and conducting further research to ensure widespread adoption are necessary tasks for the successful implementation of such systems in the future.

**Author Contributions:** Conceptualization, M.F. and A.B.F.; methodology, M.F., A.B.F. and S.E.A.; software, M.F., A.B.F., S.E.A. and M.M.I.; validation, M.F., A.B.F., S.E.A. and M.M.I.; formal analysis, M.F. and A.B.F.; investigation, M.F., A.B.F., S.E.A. and M.M.I.; resources, M.F., A.B.F., S.E.A. and M.M.I.; data curation, M.F., A.B.F., S.E.A. and M.M.I.; writing—original draft preparation, M.F. and A.B.F.; writing—review and editing, M.F., A.B.F., S.E.A. and M.M.I.; visualization, M.F., A.B.F. and S.E.A.; supervision, M.F. and M.M.I.; project administration, M.F., A.B.F. and M.M.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Publicly available data-set were analyzed in this study. This data-set can be found here: [https://universe.roboflow.com/new-workspace-f7og7/e-waste-mx8fq/dataset/1].

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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