A large part of the cost of producing Digital to Analog Converters (DACs) is related to testing, due to factors such as long time taken for analog fault diagnosis, increased testing time and expensive equipment being required for testing. Therefore, using Machine Learning (ML) based fault classification offers a promising alternative method for testing DACs compared to traditional testing, as it reduces complexity while also improving fault diagnostic accuracy. This study compares the performance of Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) to classify catastrophic faults in both 16-bit Charge Scaling and Binary Weighted DACs that have experienced process variation such as variable threshold voltage, oxide thickness and temperature. Additionally, this paper proposes a framework to eliminate the need for additional test hardware, as the simulated fault signatures are used to develop and validate classifiers for both Charge Scaling and Binary Weighted DACs. The experimental results indicate that the BPNN classified faults with an average classification accuracy of 100% for Charge Scaling DACs and 95.1% for Binary Weighted DACs. Meanwhile, the SVM classified faults with an average classification accuracy of 97.8% for Charge Scaling DACs and 94.09% for Binary Weighted DACs. The proposed PNN achieves significantly better performance with 100% classification accuracy, precision, recall and F1 score for Charge Scaling DAC; 95.5%, 95.1%, 95.5% and 95.3% for Binary Weighted DAC than both SVM and BPNN classifiers. Novelty of this work is the complete benchmarking of the performance of the three classification algorithms for methodically classifying faults in both 16-bit Charge Scaling and Binary Weighted DACs across a range of process variations and thus providing a highly reliable and inexpensive solution for automated DAC fault detection without the need for additional testing apparatus. Not applicable.
V. Govindaraj, M. Sheela, M. Muthuraja et al.· Discover Computing· 0 citations
Fake news detection focuses on identifying and preventing the spread of misleading or false information. It is crucial for maintaining the integrity of public discourse and protecting individuals from the harmful effects of misinformation. By ensuring the correctness and reliability of the information, the fake news detection hinders the loss of trust in the media, institutions, and public communication channels. The fake news detection system suggested is in the process of data acquisition where news stories are either manually or automatically retrieved from the net via web crawlers. The collected data later filters the information so it will use only credible sources. Phase two consists of the pre-processing phase using BERT, wherein the data will be tokenized and mapped into contextual embeddings that reflect the semantic meaning of words. Phase three is about engineering features using methods like TF-IDF and Word2vec to
fine-tune the embeddings and label the important textual features. The final Phase of Classification occurs using the engineered features such that BERT-generated outputs are fine-tuned and passed through softmax functions to ascertain whether the news is fake or real. This holistic and all-encompassing approach integrates advanced natural language processing with feature engineering for an effective system concerning detection of fake news accurately. The model achieved remarkable results over various phases. Training accuracy went from 75% up to those above 95% whereas test accuracy tips above 90%, soaring from below 70%. The model's performance was validated with a balanced confusion matrix and a high ROC AUC of 0.94. Throughout different phases, accuracy, precision, recall, and F1-score increased, reaching 97.0%, 96.7%, 96.8%, and 96.9%, respectively, in the final classification phase, demonstrating robust and reliable detection capabilities.
S. Gopalakrishnan, J. Thangamalar, M. Sheela et al.· International Journal of Eng...· 0 citations
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