Introduction The role of concomitant chemoradiation therapy (CTRT) or radiation therapy (RT) is not well defined in operated cases of oral squamous cell carcinoma (OSCC) with positive perineural spread. The purpose of the study was to determine whether the use of concurrent CTRT or RT would enhance the five-year disease-free survival of patients with positive perineural invasion (PNI). Materials and methods Data were analysed retrospectively from January 2014 to December 2023. Patients were placed into three groups: surgery only, surgery with RT, and surgery with concomitant CTRT. In all, 180 cases of pT1-3N0 and pT1-3N+ OSCC patients had tumour-free margins, of which 24 cases (13.4%) had perineural invasion. Based on treatment modalities, 45.8% of the cases underwent surgery with CTRT (group III), 33.3% opted for surgery with RT (group II), and 20.9% underwent surgery only (group I). Five-year recurrence-free survival was analysed among the three groups using the Kaplan-Meier model. Results There was no significant difference among the three groups in terms of recurrence (p = 0.817) or five-year survival rate (p = 0.0935). Conclusion Altogether, the data seem to indicate that radical surgical resection alone should be considered sufficient treatment for OSCC patients with pT1-3N0 disease, even in the presence of perineural invasion. Thus, it can be concluded that the addition of concomitant CTRT or RT does not significantly increase the five-year disease-free survival of patients with OSCC with positive PNI.
With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs. The word embedding representations of these language models often implicitly draw unwanted associations that form a social bias within the model. The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject. Additionally, there is sparse work done in the realm of measuring and debiasing systems for Indic languages. In our work, we attempt to evaluate and quantify the gender bias within a Hindi-English machine translation system. We implement a modified version of the existing TGBI metric based on the grammatical considerations for Hindi. We also compare and contrast the resulting bias measurements across multiple metrics for pre-trained embeddings and the ones learned by our machine translation model.
This paper presents power aware hardware implementation of multiclass Support Vector Machine on FPGA using systolic array architecture. It uses Partial reconfiguration schemes of XILINX for power optimal implementation of the design. Systolic array architecture provides efficient memory management, reduced complexity, and efficient data transfer mechanisms. Multiclass support vector machine is used as classifier for facial expression recognition system, which identifies one of six basic facial expressions such as smile, surprise, sad, anger, disgust, and fear. The extracted parameters from training phase of the SVM are used to implement testing phase of the SVM on the hardware. In the architecture, vector multiplication operation and classification of pair wise classifiers is designed. A data set of Cohn Kanade database in six different classes is used for training and testing of proposed SVM. This architecture is then partially reconfigured using difference based approach with the help of XILINX EDA tools. For feature classification power reduction is achieved using reconfiguration.
The paper proposes a novel methodology of de-noising raw electroencephalogram (EEG) data from ocular artifacts (OAs) and alpha waves extraction from motor imagery-based signals that could be further utilized for brain–computer interface (BCI)-based applications. An algorithm based on discrete wavelet transform (DWT) and nonlinear adaptive filtering for the removal of OA is advocated, with an aim of making the process computationally intelligent. This algorithm has been tested on pre-recorded EEG dataset for BCI (Dataset IIIa; obtained from the website of the BCI Competition III). To further validate the competence of the proposed method, synthetic EEG signals were created, which were fused with white Gaussian noise. A total of 20 EEG signals were generated, half of which had added noise with a signal-to-noise ratio (SNR) of 10[Formula: see text]dB and other half had added noise of 5 dBSNR. Each signal contained 1000 samples with a sampling frequency of 250[Formula: see text]Hz. An optimum bandpass filter (FIR and IIR) for extraction of alpha waves has been suggested. FIR Equiripple filter is found most appropriate for the task as it has highest SNR and computes the response faster when compared with other filters. Among different mother wavelets, Daubechies 4 wavelet obtained using statistical thresholding denoises the EEG data most successfully. Correlation and root mean square error (RMSE) parameters show that the performance of nonlinear adaptive filter developed using nonlinear Volterra series has an edge over conventional adaptive filters for the intended purpose.
In today's dynamic and complex financial landscape, accurate and reliable financial forecasting is crucial for businesses to make informed decisions, manage risks, and achieve long-term success. Artificial intelligence (AI) has emerged as a transformative force in the financial industry, offering a powerful set of tools and techniques for enhancing financial forecasting capabilities. This chapter delves into the realm of AI-driven financial forecasting, exploring the role of soft computing techniques in harnessing the power of AI for data-driven financial insights. Soft computing, a branch of computational intelligence, encompasses a suite of methodologies that mimic human reasoning and learning processes to handle complex and uncertain data. A comprehensive overview of AI-driven financial forecasting, highlighting the strengths and applications of soft computing techniques in this domain, has been envisaged beginning by introducing the fundamental concepts of AI in financial forecasting, including data pre-processing, feature selection, and model evaluation. It then delves into the specific applications of soft computing techniques in financial forecasting, exploring their use in various forecasting tasks, such as stock price prediction, exchange rate forecasting, and credit risk assessment. The transformative potential of AI-driven financial forecasting, empowered by the power of soft computing techniques, have also been underscored. The effectiveness of soft computing techniques has been shown through real-world examples and case studies, demonstrating their ability to outperform traditional forecasting methods in various financial scenarios. It also discusses the challenges and limitations of using AI in financial forecasting, emphasizing the importance of data quality, model interpretability, and ethical considerations.
Crops hold paramount significance as they serve as the primary provider of energy, nutrition, and medicinal benefits for the human population. Plant diseases, however, can negatively affect leaves during agricultural cultivation, resulting in significant losses in crop output and economic value. Therefore, it is crucial for farmers to identify crop diseases. However, this method frequently necessitates hard work, a lot of planning, and in-depth familiarity with plant pathogens. Given these numerous obstacles, it is essential to provide solutions that can easily interface with mobile and IoT devices so that our farmers can guarantee the best possible crop development. Various machine learning (ML) as well as deep learning (DL) algorithms have been created & studied for the identification of plant disease detection, yielding substantial and promising results. This article presents a novel classification method that builds on prior work by utilising attention-based feature extraction, RGB channel-based chromatic analysis, Support Vector Machines (SVM) for improved performance, and the ability to integrate with mobile applications and IoT devices after quantization of information. Several disease classification algorithms were compared with the suggested model, and it was discovered that, in terms of accuracy, Vision Transformer-based feature extraction and additional Green Chromatic Coordinate feature with SVM classification achieved an accuracy of (GCCViT-SVM) - 99.69%, whereas after quantization for IoT device integration achieved an accuracy of - 97.41% while almost reducing 4x in size. Our findings have profound implications because they have the potential to transform how farmers identify crop illnesses with precise and fast information, thereby preserving agricultural output and ensuring food security.
Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
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