Leveraging Deep Sequential Convolutional Neural Networks in Digital Transformation for Sustainable E-Waste Management in the New Digital Era

Rapid technical breakthroughs in the current digital age have contributed to the creation of electronic waste (e-waste) poses serious threats to health and the environment. This research examines the use of Deep Sequential Convolutional Neural Networks (DSCNNs) to leverage the Internet of Things (IoT) for sustainable management of electronic waste. The global e-waste dilemma is introduced with a focus on the need for innovative solutions to mitigate its impact. Existing challenges include the increasing number of discarded electronic devices, ineffective e-waste sorting, and limited recycling options. DSCNN models for their strong visual recognition capabilities, offer viable solutions by automating e-waste classification and recycling. This research explores various DSCNN architectures and their applications in e-waste processing devices to enable faster and more accurate recognition of recyclable materials. The primary goal is to develop an efficient DSCNN-based framework to enhance recycling efficiency, reduce landfill inputs, and promote sustainable practices. Preliminary findings suggest that DSCNN models significantly improve the accuracy and efficiency of e-waste sorting, paving the way for scalable and cost-effective e-waste management platforms. By addressing key environmental issues, this research advocates for the integration of AI technology into waste management processes.