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Teknologi AI dan Pembelajaran Mesin untuk Memantau dan Menyaring Kandungan Internet

By • • Knowledge, Public, Teaching • , • 4 minutes of reading

Dalam era digital yang semakin maju, isu penyebaran kandungan haram seperti pornografi, perjudian, dan ekstremisme melalui laman sesawang menjadi cabaran besar kepada kerajaan dan pihak berkuasa. Langkah-langkah untuk menyekat akses kepada laman-laman ini perlu diambil dengan teliti agar tidak menjejaskan kebebasan pengguna dan kandungan sah di internet. Salah satu pendekatan yang lebih inovatif dan berkesan berbanding kaedah penghalaan semula DNS ialah penggunaan teknologi kecerdasan buatan (AI) dan pembelajaran mesin. Teknologi ini menawarkan penyelesaian yang lebih spesifik, dengan keupayaan untuk mengenal pasti dan menyaring kandungan secara automatik, sambil meminimumkan risiko pelanggaran privasi dan kebebasan bersuara. Artikel ini akan membincangkan pelaksanaan teknologi […]

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The Dynamic Potential of Named Entity Recognition (NER) in Extracting and Analyzing Geospatial Data

By • • Knowledge, Public, Research, Teaching • , , , , , , • 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), an integral component of Natural Language Processing (NLP), plays a pivotal role in extracting meaningful information from unstructured text. This technique involves the identification and classification of specific entities within text, ranging from names of people and organizations to temporal expressions and geographic locations. The applications of NER are wide-ranging and impactful across diverse industries. In this comprehensive article, we will delve deeper into the mechanics of NER, explore its diverse applications, and focus on a specific use case: geospatial data extraction facilitated by the EntityRecognizer model. The Mechanism Behind NER At its

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Unlocking Textual Insights: The Power and Applications of Named Entity Recognition (NER)

By • • Knowledge, Public, Research, Teaching • , , , , • 3 minutes of reading

By Shahabuddin Amerudin Named Entity Recognition (NER), often referred to as entity chunking, extraction, or identification, is a vital process in the realm of Natural Language Processing (NLP). It revolves around the identification and classification of crucial information, known as entities, within text. These entities can be single words or phrases consistently referring to the same concept. Through NER, we can automatically categorize these entities into predetermined classes, such as “Person,” “Organization,” “Time,” “Location,” and more. This computational feat yields valuable insights from extensive textual data and finds its application across a plethora of scenarios. The Mechanism Behind NER NER

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Exploring the Transformative Applications of Artificial Intelligence and Machine Learning in Geospatial Technology

By • • Knowledge, Project, Public, Research, Teaching • , , , , , • 4 minutes of reading

By Shahabuddin Amerudin Abstract Geospatial technology has emerged as a pivotal discipline with far-reaching implications in numerous fields, including environmental science, geography, urban planning, and agriculture. The fusion of Artificial Intelligence (AI) and Machine Learning (ML) with geospatial analysis has ushered in an era of unprecedented advancements, elevating the capabilities of geospatial technology to new heights. This comprehensive academic article delves into the multifaceted applications of AI and ML in geospatial technology, elucidating their roles in land cover mapping, flood prediction and monitoring, precision agriculture, and traffic management. By understanding these innovative applications, readers can contribute meaningfully to the evolution

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