Published on in Vol 20, No 10 (2018): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11936, first published .
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Journals

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  4. Hogarty D, Su J, Phan K, Attia M, Hossny M, Nahavandi S, Lenane P, Moloney F, Yazdabadi A. Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review. American Journal of Clinical Dermatology 2020;21(1):41 View
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  6. Maron R, Weichenthal M, Utikal J, Hekler A, Berking C, Hauschild A, Enk A, Haferkamp S, Klode J, Schadendorf D, Jansen P, Holland-Letz T, Schilling B, von Kalle C, Fröhling S, Gaiser M, Hartmann D, Gesierich A, Kähler K, Wehkamp U, Karoglan A, Bär C, Brinker T, Schmitt L, Peitsch W, Hoffmann F, Becker J, Drusio C, Lodde G, Sammet S, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup K, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger A, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez J, Haiduk J, Alter M, Bergenthal P, Gerlach A, Holtorf C, Kindermann S, Kraas L, Felcht M, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard R, Utikal J, Winter F, Eicher L, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Mickler M, Stauner C, Thiem A. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. European Journal of Cancer 2019;119:57 View
  7. Thenault R, Kaulanjan K, Darde T, Rioux-Leclercq N, Bensalah K, Mermier M, Khene Z, Peyronnet B, Shariat S, Pradère B, Mathieu R. The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. Applied Sciences 2020;10(18):6428 View
  8. Singhal A, Shukla R, Kankar P, Dubey S, Singh S, Pachori R. Comparing the capabilities of transfer learning models to detect skin lesion in humans. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2020;234(10):1083 View
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  16. Hekler A, Utikal J, Enk A, Hauschild A, Weichenthal M, Maron R, Berking C, Haferkamp S, Klode J, Schadendorf D, Schilling B, Holland-Letz T, Izar B, von Kalle C, Fröhling S, Brinker T, Schmitt L, Peitsch W, Hoffmann F, Becker J, Drusio C, Jansen P, Lodde G, Sammet S, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schäfer S, Schäkel K, Winkler J, Wölbing P, Asper H, Bohne A, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup K, Egberts F, Erkens A, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger A, Metzner M, Motamedi R, Rosenthal A, Seidl U, Stemmermann J, Torz K, Velez J, Haiduk J, Alter M, Bär C, Bergenthal P, Gerlach A, Holtorf C, Karoglan A, Kindermann S, Kraas L, Felcht M, Gaiser M, Klemke C, Kurzen H, Leibing T, Müller V, Reinhard R, Utikal J, Winter F, Eicher L, Hartmann D, Heppt M, Kilian K, Krammer S, Lill D, Niesert A, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Gesierich A, Giner T, Glutsch V, Kerstan A, Presser D, Schrüfer P, Schummer P, Stolze I, Weber J, Drexler K, Mickler M, Stauner C, Thiem A. Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer 2019;120:114 View
  17. Brinker T, Hekler A, Enk A, von Kalle C, Huynh D. Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions. PLOS ONE 2019;14(6):e0218713 View
  18. Naeem A, Farooq M, Khelifi A, Abid A. Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities. IEEE Access 2020;8:110575 View
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  20. Keshtkar K, Keshtkar A, Safarpour A. Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis. F1000Research 2020;9:1086 View
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  22. Kadampur M, Al Riyaee S. Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked 2020;18:100282 View
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  24. Winkler J, Fink C, Toberer F, Enk A, Deinlein T, Hofmann-Wellenhof R, Thomas L, Lallas A, Blum A, Stolz W, Haenssle H. Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. JAMA Dermatology 2019;155(10):1135 View
  25. Hekler A, Utikal J, Enk A, Berking C, Klode J, Schadendorf D, Jansen P, Franklin C, Holland-Letz T, Krahl D, von Kalle C, Fröhling S, Brinker T. Pathologist-level classification of histopathological melanoma images with deep neural networks. European Journal of Cancer 2019;115:79 View
  26. de Carvalho T, Noels E, Wakkee M, Udrea A, Nijsten T. Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise. JMIR Dermatology 2019;2(1):e13376 View
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  90. Wu Y, Chen B, Zeng A, Pan D, Wang R, Zhao S. Skin Cancer Classification With Deep Learning: A Systematic Review. Frontiers in Oncology 2022;12 View
  91. Winkler J, Haenssle H. Bilderkennung mittels künstlicher Intelligenz in der Hautkrebsdiagnostik. Die Dermatologie 2022;73(11):838 View
  92. Stiff K, Franklin M, Zhou Y, Madabhushi A, Knackstedt T. Artificial intelligence and melanoma: A comprehensive review of clinical, dermoscopic, and histologic applications. Pigment Cell & Melanoma Research 2022;35(2):203 View
  93. Pasha Syed A, Anbalagan R, Setlur A, Karunakaran C, Shetty J, Kumar J, Niranjan V. Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers. BMC Bioinformatics 2022;23(1) View
  94. Sitaru S, Zink A. Digitalisierung in der Dermatoonkologie: künstliche Intelligenz zur Diagnostik. best practice onkologie 2023;18(1-2):20 View
  95. Vishwajeet Jadhav , Shivani Mane , Pranay Allepally , Neha Sonawane , Prof. Santosh Kale . Effect of Image Enhancement on Early Detection of Skin Cancer. International Journal of Advanced Research in Science, Communication and Technology 2022:11 View
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  98. Mujahid M, Rustam F, Álvarez R, Luis Vidal Mazón J, Díez I, Ashraf I. Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics 2022;12(5):1280 View
  99. Kshatri S, Singh D. Convolutional Neural Network in Medical Image Analysis: A Review. Archives of Computational Methods in Engineering 2023;30(4):2793 View
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  103. Chalkidou A, Shokraneh F, Kijauskaite G, Taylor-Phillips S, Halligan S, Wilkinson L, Glocker B, Garrett P, Denniston A, Mackie A, Seedat F. Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. The Lancet Digital Health 2022;4(12):e899 View
  104. Hussain D, Ali Naqvi R, Loh W, Lee J. Deep Learning in DXA Image Segmentation. Computers, Materials & Continua 2021;66(3):2587 View
  105. Popescu D, El-Khatib M, El-Khatib H, Ichim L. New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors 2022;22(2):496 View
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  107. Palananda A, Kimpan W. Classification of Adulterated Particle Images in Coconut Oil Using Deep Learning Approaches. Applied Sciences 2022;12(2):656 View
  108. Park S, Chien A, Lin B, Li K. FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses. Applied Sciences 2023;13(2):970 View
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  113. Lembhe A, Motarwar P, Patil R, Elias S. Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network. Procedia Computer Science 2023;218:164 View
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  119. Pawuś D, Paszkiel S. BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection. Applied Sciences 2022;12(20):10385 View
  120. Namburu A, Mohan S, Chakkaravarthy S, Selvaraj P. Skin Cancer Segmentation Based on Triangular Intuitionistic Fuzzy Sets. SN Computer Science 2023;4(3) View
  121. Adla D, Reddy G, Nayak P, Karuna G. A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection. Healthcare Analytics 2023;3:100154 View
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  124. Hartmann T, Perron R, Razavi M. Utilization of Nanoparticles, Nanodevices, and Nanotechnology in the Treatment Course of Cutaneous Melanoma. Advanced Therapeutics 2022;5(7) View
  125. Salma W, Eltrass A. Automated deep learning approach for classification of malignant melanoma and benign skin lesions. Multimedia Tools and Applications 2022;81(22):32643 View
  126. Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for Skin Cancer Detection: Scoping Review. Journal of Medical Internet Research 2021;23(11):e22934 View
  127. Montalbo F. Automating mosquito taxonomy by compressing and enhancing a feature fused EfficientNet with knowledge distillation and a novel residual skip block. MethodsX 2023;10:102072 View
  128. Vidya Lakshmi V, S. Leena Jasmine J. A Hybrid Artificial Intelligence Model for Skin Cancer Diagnosis. Computer Systems Science and Engineering 2021;37(2):233 View
  129. Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics 2022;166:104855 View
  130. Sun M, Kentley J, Mehta P, Dusza S, Halpern A, Rotemberg V. Accuracy of commercially available smartphone applications for the detection of melanoma. British Journal of Dermatology 2022;186(4):744 View
  131. Upadhyay A, Singh G, Mhatre S, Nadar P. Dog Skin Diseases Detection and Identification Using Convolutional Neural Networks. SN Computer Science 2023;4(3) View
  132. Pawuś D, Paszkiel S. Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements. Applied Sciences 2022;12(11):5762 View
  133. Alzubaidi L, Duan Y, Al-Dujaili A, Ibraheem I, Alkenani A, Santamaría J, Fadhel M, Al-Shamma O, Zhang J. Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study. PeerJ Computer Science 2021;7:e715 View
  134. Patel M. Multi Class Skin Diseases Classification Based On Dermoscopic Skin Images Using Deep Learning. International Journal of Next-Generation Computing 2022 View
  135. Gottumukkala V, Kumaran N, Sekhar V. BLSNet: Skin lesion detection and classification using broad learning system with incremental learning algorithm. Expert Systems 2022;39(9) View
  136. Raju D, Shanmugasundaram H, Sasikumar R. Fuzzy segmentation and black widow–based optimal SVM for skin disease classification. Medical & Biological Engineering & Computing 2021;59(10):2019 View
  137. Ajmal M, Khan M, Akram T, Alqahtani A, Alhaisoni M, Armghan A, Althubiti S, Alenezi F. BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification. Neural Computing and Applications 2023;35(30):22115 View
  138. Zafar M, Sharif M, Sharif M, Kadry S, Bukhari S, Rauf H. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. Life 2023;13(1):146 View
  139. Tian Y, Sun S, Qi Z, Liu Y, Wang Z. Non-tumorous facial pigmentation classification based on multi-view convolutional neural network with attention mechanism. Neurocomputing 2022;483:370 View
  140. Shehzad K, Zhenhua T, Shoukat S, Saeed A, Ahmad I, Sarwar Bhatti S, Chelloug S. A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis. Electronics 2023;12(6):1342 View
  141. Priyadharshini N, N. S, Hemalatha B, Sureshkumar C. A novel hybrid Extreme Learning Machine and Teaching–Learning-Based​ Optimization algorithm for skin cancer detection. Healthcare Analytics 2023;3:100161 View
  142. Liu S, Chen M. Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs. Sensors 2023;23(7):3366 View
  143. Naguib S, Hamza H, Hosny K, Saleh M, Kassem M. Classification of Cervical Spine Fracture and Dislocation Using Refined Pre-Trained Deep Model and Saliency Map. Diagnostics 2023;13(7):1273 View
  144. To H, Nguyen H, Le H, Le H, Quan T. MetaAttention model: a new approach for skin lesion diagnosis using AB features and attention mechanism. Biomedical Physics & Engineering Express 2023;9(4):045008 View
  145. Xu D, Zhou B, Zhang J, Li C, Guan C, Liu Y, Li L, Li H, Cui L, Xu L, Liu H, Zhen L, Xu Y. Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG. Renal Failure 2023;45(1) View
  146. Juan C, Su Y, Wu C, Yang C, Hsu C, Hung C, Chen Y. Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation. Scientific Reports 2023;13(1) View
  147. Kuo K, Talley P, Chang C. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  148. Liu Q, Zhang J, Bai Y. Mapping the landscape of artificial intelligence in skin cancer research: a bibliometric analysis. Frontiers in Oncology 2023;13 View
  149. Kumar R, Sood P, Nirala R, Ade R, Sonaji A. Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose. Journal for Research in Applied Sciences and Biotechnology 2023;2(5):51 View
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  151. Zhang L, Xu R, Zhao J. Learning technology for detection and grading of cancer tissue using tumour ultrasound images1. Journal of X-Ray Science and Technology 2024;32(1):157 View
  152. Shafi N, Costantine J, Kanj R, Tawk Y, Ramadan A, Kurban M, Rahal J, Eid A. A Portable Non-Invasive Electromagnetic Lesion-Optimized Sensing Device for the Diagnosis of Skin Cancer (SkanMD). IEEE Transactions on Biomedical Circuits and Systems 2023;17(3):558 View
  153. Hussain M, Fiza M, Khalil A, Siyal A, Dharejo F, Hyder W, Guzzo A, Krichen M, Fortino G. Transfer learning-based quantized deep learning models for nail melanoma classification. Neural Computing and Applications 2023;35(30):22163 View
  154. DİMİLİLER K, SEKEROGLU B. Skin Lesion Classification Using CNN-based Transfer Learning Model. Gazi University Journal of Science 2023;36(2):660 View
  155. Ogundokun R, Li A, Babatunde R, Umezuruike C, Sadiku P, Abdulahi A, Babatunde A. Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models. Bioengineering 2023;10(8):979 View
  156. AlSuwaidan L. Deep Learning Based Classification of Dermatological Disorders. Biomedical Engineering and Computational Biology 2023;14 View
  157. Singareddy S, SN V, Jaramillo A, Yasir M, Iyer N, Hussein S, Nath T. Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus 2023 View
  158. Ajuwon B, Awotundun O, Richardson A, Roper K, Sheel M, Rahman N, Salako A, Lidbury B. Machine learning prediction models for clinical management of blood-borne viral infections: a systematic review of current applications and future impact. International Journal of Medical Informatics 2023;179:105244 View
  159. Rostamzadeh-Renani R, Jasim D, Baghoolizadeh M, Rostamzadeh-Renani M, Andani H, Salahshour S, Baghaei S. Multi-objective optimization of rheological behavior of nanofluids containing CuO nanoparticles by NSGA II, MOPSO, and MOGWO evolutionary algorithms and group method of data handling artificial neural networks. Materials Today Communications 2024;38:107709 View
  160. Fernandes J, Teles A, Fernandes T, Lima L, Balhara S, Gupta N, Teixeira S. Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review. Journal of Clinical Medicine 2023;13(1):180 View
  161. Lv J, Zhang R, Gu Q, Hemayet Uddin M, Jiang X, Qi J, Si G, Ou Q. Metasurfaces and their intelligent advances. Materials & Design 2024;237:112610 View
  162. Shakeel C, Khan S. Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2024;238(2):132 View
  163. Hossain M, Hossain M, Arefin M, Akhtar F, Blake J. Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble. Diagnostics 2023;14(1):89 View
  164. Keskenler M, Çelik E, Dal D. A New Multi-Layer Machine Learning (MLML) Architecture for Non-invasive Skin Cancer Diagnosis on Dermoscopic Images. Journal of Electrical Engineering & Technology 2024;19(4):2739 View
  165. Benzyane M, Azrour M, Zeroual I, Agoujil S. Investigating the Influence of Convolutional Operations on LSTM Networks in Video Classification. Data and Metadata 2023;2:152 View
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Books/Policy Documents

  1. Singh N, Kaur A. Interdisciplinary Approaches to Altering Neurodevelopmental Disorders. View
  2. Martorell-Marugán J, Tabik S, Benhammou Y, del Val C, Zwir I, Herrera F, Carmona-Sáez P. Computational Biology. View
  3. El-khatib H, Popescu D, Ichim L. Advances in Computational Intelligence. View
  4. Young K, Booth G, Simpson B, Dutton R, Shrapnel S. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. View
  5. Sivasubramanian K, Xing L. LED-Based Photoacoustic Imaging. View
  6. Aldwgeri A, Abubacker N. Advances in Visual Informatics. View
  7. Janoria H, Minj J, Patre P. Intelligent Data Communication Technologies and Internet of Things. View
  8. Iglesias-Puzas Á, Boixeda P. Photography in Clinical Medicine. View
  9. Dwivedi P, Sandhu S, Zeeshan M, Sarkar P. Soft Computing Techniques and Applications. View
  10. Mehra A, Bhati A, Kumar A, Malhotra R. Emerging Technologies in Data Mining and Information Security. View
  11. Dutta A, Kamrul Hasan M, Ahmad M. Proceedings of International Joint Conference on Advances in Computational Intelligence. View
  12. Gao Y, Tang Z, Zhou M, Metaxas D. Information Processing in Medical Imaging. View
  13. Das T, Kumar V, Prakash A, Lynn A. Skin Cancer: Pathogenesis and Diagnosis. View
  14. Nunnari F, Kadir M, Sonntag D. Machine Learning and Knowledge Extraction. View
  15. Jaworek-Korjakowska J, Yap M, Bhattacharjee D, Kleczek P, Brodzicki A, Gorgon M. State of the Art in Neural Networks and Their Applications. View
  16. Barbhuiya R, Ahmad N, Akram W. Computational Intelligence in Oncology. View
  17. Biswal P, Saha M, Jaiswal N, Rout M. The New Advanced Society. View
  18. Panigrahi A, Bhutia S, Sahu B, Galety M, Mohanty S. Disruptive Technologies for Big Data and Cloud Applications. View
  19. Orrin E, Cassidy P, Kulkarni R, Berry E, Leachman S. Melanoma in Clinical Practice. View
  20. Chauhan A, Hasija Y. Proceedings of Emerging Trends and Technologies on Intelligent Systems. View
  21. Kortała M, Jaworska T, Ganzha M, Paprzycki M. Big-Data-Analytics in Astronomy, Science, and Engineering. View
  22. Keerthana D, Nath M. Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences. View
  23. Agilandeeswari L, Bansal A, Sasank P, Yasasvi K. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). View
  24. Sai Venkatesh C, Meriga C, Geethika M, Lakshmi Gayatri T, Aruna V. High Performance Computing and Networking. View
  25. Pundhir A, Agarwal A, Dadhich S, Raman B. Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging. View
  26. Pike A, Benkli B, Gilani S, Hirani S. Substance Use and Addiction Research. View
  27. Dhiman A, Chauhan N. Proceedings of the International Health Informatics Conference. View
  28. Karpagam G, Keerthna M, Naresh K, Sairam Vaidya M, Karthikeyan T, Mohideen S. Artificial Intelligence for Sustainable Applications. View
  29. Singh C, Nischitha , Shetty S, Bekal A, Bhat S, Badiger M. Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. View
  30. Deepa Nivethika S. , Srinivasan D, SenthilPandian M. , Paulraj P, Ashokkumar N, Hariharan K. , Maneesh Vijay V. I. , Raghuram T. . Neuromorphic Computing Systems for Industry 4.0. View
  31. Al-Qaisi A, George L. Proceedings of Eighth International Congress on Information and Communication Technology. View
  32. Goindi S, Thakur K, Kapoor D. Advances in IoT and Security with Computational Intelligence. View
  33. Surówka G. Artificial Intelligence and Soft Computing. View
  34. Shashidhara G, Agarwal R, Suryavamshi J. Intelligent Systems and Machine Learning. View
  35. Obaid A, Suman Rajest S, Silvia Priscila S, Shynu T, Ettyem S. Proceedings of Data Analytics and Management. View
  36. Benzyane M, Azrour M, Zeroual I, Agoujil S. Artificial Intelligence, Data Science and Applications. View
  37. Gupta A, Kuresan H, Talha A, Abhinav P, Dhanalakshmi S. Human-Centric Smart Computing. View
  38. King I, Meng H, Lam T. Artificial Intelligence in Medicine. View
  39. Siddarth C, Poreddy A, Kokil P. Computer Vision and Image Processing. View
  40. Dhage S, Chawan H, Hoskote A, Dabholkar A, Deshmukh V. Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation. View
  41. Borghesi A, Calegari R. AI for Health Equity and Fairness. View
  42. Haghshenas F, Krzyżak A, Osowski S. Artificial Neural Networks in Pattern Recognition. View
  43. Mahmud T, Barua K, Chakma K, Chakma R, Sharmen N, Kaiser M, Hossain M, Hossain M, Andersson K. Proceedings of Trends in Electronics and Health Informatics. View