arXiv subjects

Sayed Mahbub Hasan Amiri

Publications and source records attributed to Sayed Mahbub Hasan Amiri.

5 recordsLinked to original sources

arXiv2026

MicroPython and CircuitPython: Pythons Quiet Takeover of IoT and Robotics

Background: Python has become the dominant language in software and data science, yet embedded systems have remained tied to C/C++ due to performance and memory constraints. MicroPython and CircuitPython are changing this by bringing Python to microcontrollers, lowering barriers for IoT and robotics development. Aim: This article examines whether these platforms are achieving a quiet takeover of embedded systems, focusing on ecosystem growth, practical applications, performance trade-offs, educational adoption, and prospects. Methods: A mixed-methods design was used, including quantitative analysis of GitHub, Stack Overflow, and Google Trends data; curation of case studies from Hackster.io, Hackaday.io, and the Adafruit Learning System; and original benchmarks on ESP32 and Raspberry Pi Pico comparing MicroPython, CircuitPython, and Arduino C++ across GPIO, I2C, SPI, Wi-Fi, and memory usage. Results: Metrics show sustained growth, with MicroPython supporting over 200 boards and CircuitPython over 400. Benchmarks reveal 10-20 times slower I/O and four to six times higher memory use than C, but performance remains adequate for common sensor and network tasks. Case studies demonstrate successful deployment in home automation, robotics, wearables, agriculture, and professional prototyping. Education emerges as a primary adoption driver. Conclusions: Python is not replacing C/C++; rather, it is becoming the default prototyping and educational language for embedded systems. Continued hardware improvements, better tooling, and standardization will likely deepen this trend. The article offers balanced, evidence-based insights for developers, educators, and technology decision-makers.

cs.PL↗

arXiv2025

Enhancing Python Programming Education with an AI-Powered Code Helper: Design, Implementation, and Impact

This is the study that presents an AI-Python-based chatbot that helps students to learn programming by demonstrating solutions to such problems as debugging errors, solving syntax problems or converting abstract theoretical concepts to practical implementations. Traditional coding tools like Integrated Development Environments (IDEs) and static analyzers do not give robotic help while AI-driven code assistants such as GitHub Copilot focus on getting things done. To close this gap, our chatbot combines static code analysis, dynamic execution tracing, and large language models (LLMs) to provide the students with relevant and practical advice, hence promoting the learning process. The chatbots hybrid architecture employs CodeLlama for code embedding, GPT-4 for natural language interactions, and Docker-based sandboxing for secure execution. Evaluated through a mixed-methods approach involving 1,500 student submissions, the system demonstrated an 85% error resolution success rate, outperforming standalone tools like pylint (62%) and GPT-4 (73%). Quantitative results revealed a 59.3% reduction in debugging time among users, with pre- and post-test assessments showing a 34% improvement in coding proficiency, particularly in recursion and exception handling. Qualitative feedback from 120 students highlighted the chatbots clarity, accessibility, and confidence-building impact, though critiques included occasional latency and restrictive code sanitization. By balancing technical innovation with pedagogical empathy, this research provides a blueprint for AI tools that prioritize educational equity and long-term skill retention over mere code completion. The chatbot exemplifies how AI can augment human instruction, fostering deeper conceptual understanding in programming education.

cs.SE↗

arXiv2025

Green Computing: The Ultimate Carbon Destroyer for a Sustainable Future

Green computing represents a critical pathway to decarbonize the digital economy while maintaining technological progress. This article examines how sustainable IT strategies including energy-efficient hardware, AI-optimized data centres, and circular e-waste systems can transform computing into a net carbon sink. Through analysis of industry best practices and emerging technologies like quantum computing and biodegradable electronics, we demonstrate achievable reductions of 40-60% in energy consumption without compromising performance. The study highlights three key findings: (1) current solutions already deliver both environmental and economic benefits, with typical payback periods of 3-5 years; (2) systemic barriers including cost premiums and policy fragmentation require coordinated action; and (3) next-generation innovations promise order-of-magnitude improvements in efficiency. We present a practical framework for stakeholders from corporations adopting renewable-powered cloud services to individuals extending device lifespans to accelerate the transition. The research underscores computing's unique potential as a climate solution through its rapid innovation cycles and measurable impacts, concluding that strategic investments in green IT today can yield disproportionate sustainability dividends across all sectors tomorrow. This work provides both a compelling case for urgent action and a clear roadmap to realize computing's potential as a powerful carbon destruction tool in the climate crisis era.

cs.CY↗

arXiv2025

The Carbon Cost of Conversation, Sustainability in the Age of Language Models

Large language models (LLMs) like GPT-3 and BERT have revolutionized natural language processing (NLP), yet their environmental costs remain dangerously overlooked. This article critiques the sustainability of LLMs, quantifying their carbon footprint, water usage, and contribution to e-waste through case studies of models such as GPT-4 and energy-efficient alternatives like Mistral 7B. Training a single LLM can emit carbon dioxide equivalent to hundreds of cars driven annually, while data centre cooling exacerbates water scarcity in vulnerable regions. Systemic challenges corporate greenwashing, redundant model development, and regulatory voids perpetuate harm, disproportionately burdening marginalized communities in the Global South. However, pathways exist for sustainable NLP: technical innovations (e.g., model pruning, quantum computing), policy reforms (carbon taxes, mandatory emissions reporting), and cultural shifts prioritizing necessity over novelty. By analysing industry leaders (Google, Microsoft) and laggards (Amazon), this work underscores the urgency of ethical accountability and global cooperation. Without immediate action, AIs ecological toll risks outpacing its societal benefits. The article concludes with a call to align technological progress with planetary boundaries, advocating for equitable, transparent, and regenerative AI systems that prioritize both human and environmental well-being.

cs.CY↗

arXiv2025

Hear Your Code Fail, Voice-Assisted Debugging for Python

This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugging, while enabling 78% faster error identification through vocalized exception classification and contextualization. The system achieves sub-1.2 second voice latency with under 18% CPU overhead during exception handling, vocalizing error types and consequences while displaying interactive tracebacks with documentation deep links. Criteria validate compatibility across Python 3.7+ environments on Windows, macOS, and Linux platforms. Needing only two lines of integration code, the plugin significantly boosts availability for aesthetically impaired designers and supports multitasking workflows through hands-free error medical diagnosis. Educational applications show particular promise, with pilot studies indicating 45% faster debugging skill acquisition among novice programmers. Future development will incorporate GPT-based repair suggestions and real-time multilingual translation to further advance auditory debugging paradigms. The solution represents a fundamental shift toward human-centric error diagnostics, bridging critical gaps in programming accessibility while establishing new standards for cognitive efficiency in software development workflows.

cs.PL↗