Arsalan Mohammad (21 results)

Soft cover. Condition: Near Fine. 1st Edition. Catalog for exhibition at Meem Gallery, Dubai, March 13 - May 10, 2016. Wraps, 68 pgs (unpaginated). Measures 8.25" x 10.25". Includes full-page images of etchings, paintings, etching plates, artist's staatement, interview, biography, CV, etc. Very light edge wear, near fine.

Paperback. Condition: Brand New. 134 pages. 6.00x0.31x9.00 inches. In Stock.

hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

SOFTCOVER. Condition: Fine. 1st Edition. Large 4to. in colour printed stiff card covers, 140pp on glossy art paper, colour plates, etc. Dual text in English and German . [CONDITION: An almost AS NEW unmarked copy (short flat crease to top corner tip of front cover) ] . __To see more of our Art Monographs etc type DbbARTIST in the Keywords search box . . We always ship in STRONG PROTECTIVE CARD PARCELS.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condition: new. Paperback. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: new. Hardcover. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. Detecting Hate Speech in Human and AI-Generated Content | Techniques, Bias Mitigation, and Ethical Considerations | Mohammad Arsalan (u. a.) | Taschenbuch | Englisch | 2025 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337330648 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Hardcover. Condition: new. Hardcover. As digital communication becomes increasingly pervasive, the detection of hate speech in both human and AI-generated content has emerged as a critical concern for online safety. The use of harmful language across social media platforms and content generated by language models poses significant challenges in identifying toxic discourse. Traditional moderation methods often fall short in recognizing nuances prompting the integration of machine learning and natural language processing techniques to enhance detection accuracy. This evolving field underscores the need for robust systems capable of distinguishing between free expression and harmful language in this era. Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations addresses the pressing challenge of hate speech detection across both AI-generated and human-generated content. It fills a crucial gap, providing a dual-focused approach to detect and manage hate speech effectively in this new, mixed-content landscape. Covering topics such as deepfakes, moderation, and social media, this book is an excellent resource for researchers, academicians, students, policymakers, and more. "The purpose of this book is to tackle the pressing challenge of hate speech detection across both AI-generated and human-generated content"-- Provided by publisher This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condition: Neu. Detecting Hate Speech in Human and AI-Generated Content | Techniques, Bias Mitigation, and Ethical Considerations | Mohammad Arsalan (u. a.) | Buch | Englisch | 2025 | IGI GLOBAL SCIENTIFIC PUBLISHING | EAN 9798337330631 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.