iosr-JCE   Volume-26 ~ Issue-5 ~ Sep. - Oct. 2024

Abstract: Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent of deep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more. This study proposes a methodological combination...

Keywords: Electroencephalography, Antarang, EmoEngine.

[1] Roy, Y., H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, And J. Faubert. 2019. "Deep Learning-Based Electroencephalography Analysis: A Systematic Review." J Neural Eng 16, No. 5 (August): 051001.
[2] Gong, Shuxiao, Kaibo Xing, Andrzej Cichocki, And Junhua Li. 2020. "Deep Learning In Eeg: Advance Of The Last Ten-Year Critical Period." Arxiv Abs/2011.11128.
[3] D. Sammler, M. Grigutsch, T. Fritz, And S. Koelsch, "Music And Emotion: Electrophysiological Correlates Of The Processing Of Pleasant And Unpleasant Music," Psychophysiology, Vol. 44, No. 2, Pp. 293-304, 2007.
[4] E. Kroupi, A. Yazdani, And T. Ebrahimi, "Eeg Correlates Of Differentemotional States Elicited During Watching Music Videos," In Affective Computing And Intelligent Interaction, 2011, Pp. 457-466.
[5] L. I. Aftanas, A. A. Varlamov, S. V. Pavlov, V. P. Makhnev, And N. V. Reva, "Affective Picture Processing: Event-Related Synchronization Within Individually Defined Human Theta Band Is Modulated By Valence Dimension," Neuroscience Letters, Vol. 303, No. 2, Pp. 115-118, 2001.

Abstract: The primary objective for the AI subfield of "generative artificial intelligence" is to develop systems that may produce new, novel and creative content including text, photos, audio, music, and movies. These models are able to generate fresh content that nearly mimics realistic content created by humans by utilizing deep learning techniques. These models of Gen AI have gained significant importance in research and have plethora of applications in wide varieties of fields. The impact of GenAI is not just on abled but also on disabled communities who are sometimes unnoticed. This survey provides a thorough........

Keywords: Generative Artificial Intelligence, Deep learning techniques, applications, challenges, future scope.

[1] The Impact, Advancements And Applications Of Generative Ai Authors Balagopal Ramdurai , Senior Member- Ieee, Researcher & Product Innovator & Prasanna Adhithya (Editor)
[2] Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In International Conference On Learning Representations (Iclr). Retrieved From Https://Arxiv.Org/Abs/1312.6114
[3] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative Adversarial Networks. In Advances In Neural Information Processing Systems (Nips) (Pp. 2672-2680). Retrieved From Https://Arxiv.Org/Abs/1406.2661
[4] Maria Bordukova, Nikita Makarov, Raul Rodriguez-Esteban, Fabian Schmich & Michael P. Menden (2024) Generative Artificial Intelligence Empowers Digital Twins In Drug Discovery And Clinical Trials, Expert Opinion On Drug Discovery, 19:1, 33-42, Doi: 10.1080/17460441.2023.2273839
[5] Deepak Giri, Indiana University Indianapolis, United States Erin Brady, Indiana University Indianapolis, United States A Democratic Platform For Engaging With Disabled Community In Generative Ai Development

Abstract: Plant diseases are becoming more common, which is a serious threat to profitability of agriculture worldwide and requires the creation of reliable and efficient disease detection technologies. This work uses machine learning (ML) and deep learning (DL) approaches to give an extensive assessment of previous research on plant leaf disease identification. We examine and evaluate the many approaches used in the last several research, emphasizing the advantages, disadvantages, and useful uses of each. The study covers both sophisticated deep learning designs........

Keywords: Convolution Neural Network, Deep Learning Machine Learning, Plant Disease Detection, Support Vector Machine.

[1] P. Chaitanya Reddy, R. M. S. Chandra, P. Vadiraj, M. Ayyappa Reddy, T. R. Mahesh, And G. Sindhu Madhuri, "Detection Of Plant Leaf-Based Diseases Using Machine Learning Approach," In 2021 Ieee International Conference On Computation System And Information Technology For Sustainable Solutions (Csitss), 2021.
[2] D. Mishra, A. Pandey, Deepanshu, And V. Sharma, "Plant Image Disease Detection Using Deep Learning," In 2023 4th International Conference On Smart Electronics And Communication (Icosec), 2023.
[3] M. Belmir, W. Difallah, And A. Ghazli, "Plant Leaf Disease Prediction And Classification Using Deep Learning," In 2023 International Conference On Decision Aid Sciences And Applications (Dasa), 2023.
[4] Shruti Et Al., "Plant Leaf Disease Detection Using Machine Learning," In 2023 1st International Conference On Circuits, Power And Intelligent Systems (Ccpis), 2023.
[5] V. Binnar, S. Sharma, R. Doriya, B. Soni, A. Shukla, And X. Z. Gao, "Plant Leaf Diseases Detection Using Deep Learning Algorithms," In Machine Learning, Image Processing, Vol. 946, Singapore: Springer, 2023

Abstract: This article has examined how the aspect of language use creates intent in the riddles arising from social change in Tanzania. The primary data used in the analysis, discussion and presented in this article were found in the library by the method of careful reading. The target selection technique was used to find selected riddles by referring to the specific objective of the article aimed at investigating how the use of language creates intent in riddles resulting from social change. A review of the documents used to verify the primary data was obtained through electronic and library methods.........

Keywords: Riddles, Language Use, Construction, Mission and New Historicism Theory.

[1] Abrams, M. H. (1999). A Glossary Of Literary Terms. New York: Harcourt And Brace.
[2] Ally, H. (2001). Uchambuzi Na Ukusanyaji Wa Kitendawili Vya Kiha. Dar Es Salaam, Chuo Kikuu Cha Dar Es Saalam.
[3] Finnegan, R. (1970). Oral Literature In Africa. London: Longman Press Limited.
[4] Furlong, N. (2000) Research Methods And Statistics: An Intergrated Approach. New York: Harcourt Collect Publishers.
[5] Gabriel, I. (2006) Disabled People The Forgotten Group In The Struggle To Combat Hiv/Aids: Visually And Hearing Impaired People Kinondoni. Tasnifu Ya Umahiri, Chuo Kikuu Cha Dar Es Salaam, Dar Es Salaam.

Abstract: A neurological condition known as an autism spectrum disorder (ASD) profoundly impacts an individual's lifelong ability to engage and interact with others. ASD is an illness that can be recognized at early stages in a person's life and is often classified as a "behavioral disease" due to the presence of numerous symptoms that frequently manifest within the first two years of life, as per what most people believe about autism theory, these challenges typically emerge during childhood and endure into adolescence and adulthood [1].........

Keywords: Autism, Convolutional neural network (CNN); Artificial Neural Network (ANN); K- Nearest Neighbors (KNN); Logistic Regression (LR); Support Vector Machine (SVM) [5].

[1] S. M. Mahedy Hasan, M. P. Uddin, M. A. Mamun, M. I. Sharif, A. Ulhaq And G. Krishnamoorthy, "A Machine Learning Framework For Early-Stage Detection Of Autism Spectrum Disorders," In Ieee Access, Vol. 11, Pp. 15038-15057, 2023, Doi: 10.1109/Access.2022.3232490.
[2] K. Vakadkar, D. Purkayastha, And D. Krishnan, "Detection Of Autism Spectrum Disorder In Children Using Machine Learning Techniques,"*Sn Computer Science*, Vol. 2, No. 5, P. 386, Jul. 22, 2021. Doi: 10.1007/S42979-021-00776-5.
[3] D. D. Khudhur, S. D. Khudhur, "The Classification Of Autism Spectrum Disorder By Machine Learning Methods On Multiple Datasets For Four Age Groups," *Measurement: Sensors*, Vol. 27, 2023, Pp. 100774. Issn2665-9174. Doi: 10.1016/J.Measen.2023.100774.
[4] S. Liang, A. Q. M. Sabri, F. Alnajjar And C. K. Loo, "Autism Spectrum Self-Stimulatory Behaviors Classification Using Explainable Temporal Coherency Deep Features And Svm Classifier," In Ieee Access, Vol. 9, Pp. 34264-34275, 2021, Doi: 10.1109/Access.2021.3061455.
[5] F. Ke, S. Choi, Y. H. Kang, K. -A. Cheon And S. W. Lee, "Exploring The Structural And Strategic Bases Of Autism Spectrum Disorders With Deep Learning," In Ieee Access, Vol. 8, Pp. 153341-153352, 2020, Doi: 10.1109/Access.2020.3016734.

Abstract: Convolutional Neural Networks (CNNs) have made significant strides in various domains such as image classifica- tion, speech recognition, automotive software engineering and neuroscience. In this study we leverage CNNs for the automated detection of Autism Spectrum Disorder using brain imaging data, specifically we utilize resting-state functional Magnetic Resonance Imaging (f-MRI) data from the Autism Brain Imaging Exchange (ABIDE) dataset. To identify ASD patients, our approach relies on analyzing patterns of functional connectivity to classify ASD and control subjects. Experimental results demonstrate that our proposed model achieves an accuracy....

Keywords - Autism Spectrum Disorder (ASD), Convolutional neural network (CNN); Artificial Neural Network (ANN); K- Nearest Neighbors (KNN); Logistic Regression (LR); Support Vector Machine (SVM)

[1] Feng, M.; Xu, J. Detection Of ASD Children Through Deeplearning Application Of Fmri. Children 2023, 10, 1654. Https://
Doi.Org/10.3390/Children10101654
[2] N. Yakolli, V. Anusha, A. A. Khan, S. A. Shubhashree, And S. Chatterjee, "Enhancing The Diagnosis Of Autism Spectrum Disorder Using Phenotypic, Structural, And Functional MRI Data," Innovations In Systems And Software Engineering, Pp. 1-12, 2023.
[3] M. Ingalhalikar, S. Shinde, A. Karmarkar, A. Rajan, D. Rangaprakash, And G. Deshpande, "Functional Connectivity-Based Prediction Of Autism On-Site Harmonized ABIDE Dataset," IEEE Transactions On Biomedical Engineering, Vol. 68, No. 12, Pp. 3628-3637, 2021
[4] Y. Fu, J. Zhang, Y. Li, J. Shi, Y, Zou, H. Guo, Y. Li, Z. Yao, Y. Wang, And B. Hu, "A Novel Pipeline Leveraging Surface-Based Features Of Small Subcortical Structures To Classify Individuals With Autism Spectrum Disorder," Progress In Neuro-Psychopharmacology And Biological Psychiatry, Vol. 104, Pp. 109989, 2021.
[5] C. Ray, H. Kumar Tripathy, S. Mishra, "Assessment Of Autistic Disorder Using Machine Learning Approach. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (Eds) Intelligent Computing And Communication," ICICC 2019. Advances In Intelligent Systems And Computing, Vol 1034. 2020. Springer, Singapore.

Abstract: Today, the continent of Africa is at an epoch in its digital transformation journey. As the continent embraces modern technologies, it often encounters a disconnect between them and its indigenous cultural practices and environmental contexts. This paper proposes a radical shift from the Western-centric technological paradigms to a model that integrates Indigenous Knowledge Systems (IKS) with digital transformation efforts. This study offers a unique view on sustainable and culturally congruent technological development by examining the limitations of current digital.......

Keywords: Indigenous Knowledge Systems, Digital Transformation, Africa, Cultural Erosion, Sustainability, Technological Sovereignty, Future Technologies

[1] Adebayo, T. (2023). Culturally-Aware Ai: Integrating Local Knowledge In Africa. Journal Of Ai Ethics, 12(3), 234-250.
[2] Adeoye, A., & Adeoye, B. (2023). Data Sovereignty In Africa: Challenges And Opportunities. African Journal Of Information Ethics, 9(1), 112-130.
[3] Berkes, F. (2018). Sacred Ecology: Traditional Ecological Knowledge And Resource Management. Routledge.
[4] Boaventura De Sousa, S. (2014). Epistemologies Of The South: Justice Against Epistemicide. Routledge.
[5] Mignolo, W. (2011). The Darker Side Of Western Modernity: Global Futures, Decolonial Options. Duke University Press.
[6] Mbogo, M. (2023). Bias In Ai Credit Scoring. African Financial Review, 19(3), 89-102

Abstract: Photorealism has been the defining goal of computer graphics for half a century. A look at even the most sophisticated real-time games will quickly reveal that photorealism has not been achieved. An ineffable difference in the appearance of simulation and reality remains. In recent years, a complementary set of techniques has been developed in computer vision and machine learning. These techniques, based on deep learning, convolutional networks, and adversarial training, bypass physical modeling of geometric layout, material appearance, and light transport. Instead, images are synthesized by convolutional networks trained on large datasets....

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[5] A. Knapitsch, J. Park, Q. Zhou, And V. Koltun, "Tanks And Temples: Benchmarking Large-Scale Scene Reconstruction," Acm Trans. Graph, Vol. 36, No. 4, Pp. 1–78, 2017..