Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India.Comment: This is the pre-print of the paper that has been accepted for publication in the Inderscience Journal "International Journal of Business Forecasting and Marketing Intelligence". The paper is 35 pages long, and it contains 37 figures and 20 tables. This is, however, not the final published versio

Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation

In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed scheme is designed to improve the effectiveness of contextual perturbations by integrating saliency, attention, and semantic similarity. Traditional adversarial attack methods often struggle to maintain semantic consistency and coherence while effectively deceiving target models. Our proposed approach addresses these challenges by incorporating a three-pronged strategy for word selection and perturbation. First, we utilize a saliency-based word selection to prioritize words for modification based on their importance to the model's prediction. Second, attention mechanisms are employed to focus perturbations on contextually significant words, enhancing the attack's efficacy. Finally, an advanced semantic similarity-checking method is employed that includes embedding-based similarity and paraphrase detection. By leveraging models like Sentence-BERT for embedding similarity and fine-tuned paraphrase detection models from the Sentence Transformers library, the scheme ensures that the perturbed text remains contextually appropriate and semantically consistent with the original. Empirical evaluations demonstrate that SASSP generates adversarial examples that not only maintain high semantic fidelity but also effectively deceive state-of-the-art natural language processing models. Moreover, in comparison to the original scheme of contextual perturbation CLARE, SASSP has yielded a higher attack success rate and lower word perturbation rate.Comment: The paper is 12 pages long. and it contains 5 tables. It is the pre-reviewed version of the paper that has been accepted for oral presentation and publication in the 5th International Conference on Data Science and Applications which will be organized in Jaipur, India from July 17 to 19, 2024. This is not the final versio

Revisiting The Role Of Sbi In Digitization Mapping: A CSR Initiative

Digitalization has inaugurated a new approach to publicize socialization in all types of organizations, generally, and businesses specifically. Stakeholders now favour companies that adhere to social standards and virtues. The companies comprehend that it is essential for the advertising of their CSR activities so that the viewing public can fully comprehend that they are engaged with a business that delivers facilities that benefit society. With the advancement of technology, cyberspace provides several portals upon which content can be posted. Once the viewers are engaged with the organization, the word is dispersed in such a way that the company garners prominence. Using such an added benefit, the company uses this framework to endorse the business to its full extent too. This is accomplished with the assistance of digital marketing. This paper emphasizes the marketing potential of CSR efforts for businesses and the necessity of social media advertising of such programs in the current technology era. This essay also discusses how using digital channels like social media, websites, blogs, and emails to promote CSR initiatives can be advantageous to the business in the long run. As a result, this article aims to connect the CSR principle to marketing by categorizing various theoreticalviewpoints that suggest a connection between the two concepts. The present paper is an attempt to capture the extent of digitalisation of services and the subsequent impact on the CSR performances of the largest bank in the Indian sub-continent- State Bank of India. The paper tries to analyse the growth of CSR activities vis a vis the rate of digital services provided by the bank

Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model

This work delved into the realm of automatic text generation, exploring a variety of techniques ranging from traditional deterministic approaches to more modern stochastic methods. Through analysis of greedy search, beam search, top-k sampling, top-p sampling, contrastive searching, and locally typical searching, this work has provided valuable insights into the strengths, weaknesses, and potential applications of each method. Each text-generating method is evaluated using several standard metrics and a comparative study has been made on the performance of the approaches. Finally, some future directions of research in the field of automatic text generation are also identified.Comment: This report pertains to the Capstone Project done by Group 5 of the Fall batch of 2023 students at Praxis Tech School, Kolkata, India. The reports consists of 57 pages and it includes 17 figures and 8 tables. This is the preprint which will be submitted to IEEE CONIT 2024 for revie

CURRICULUM DEVELOPMENT IN LANGUAGE STUDIES

In this paper the field of language curriculum development is defined as encompassing the processes of needs analysis, goal setting, syllabus design, methodology and evaluation. Then the different factors affecting modern curriculum in today’s world is discussed in detail. Needs analysis is discussed in relation to an important facet for language program planning. Different approaches to the planning of program objectives in language teaching are also talked about. Some ideas to fit in modern language curriculum are also discussed.</jats:p

Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent

Adversarial attacks against deep learning models represent a major threat to the security and reliability of natural language processing (NLP) systems. In this paper, we propose a modification to the BERT-Attack framework, integrating Projected Gradient Descent (PGD) to enhance its effectiveness and robustness. The original BERT-Attack, designed for generating adversarial examples against BERT-based models, suffers from limitations such as a fixed perturbation budget and a lack of consideration for semantic similarity. The proposed approach in this work, PGD-BERT-Attack, addresses these limitations by leveraging PGD to iteratively generate adversarial examples while ensuring both imperceptibility and semantic similarity to the original input. Extensive experiments are conducted to evaluate the performance of PGD-BERT-Attack compared to the original BERT-Attack and other baseline methods. The results demonstrate that PGD-BERT-Attack achieves higher success rates in causing misclassification while maintaining low perceptual changes. Furthermore, PGD-BERT-Attack produces adversarial instances that exhibit greater semantic resemblance to the initial input, enhancing their applicability in real-world scenarios. Overall, the proposed modification offers a more effective and robust approach to adversarial attacks on BERT-based models, thus contributing to the advancement of defense against attacks on NLP systems.Comment: This paper is the pre-reviewed version of our paper that has been accepted for oral presentation and publication in the 4th IEEE ASIANCON. The conference will be organized in Pune, INDIA from August 23 to 25, 2024. The paper consists of 8 pages and it contains 10 tables. It is NOT the final camera-ready version that will be in IEEE Xplor

APPICATION OF GALECTIN3 , A NOVEL IMMUNOSTAIN, IN PROSTATIC CARCINOMA TO ASSESS ITS PATTERN OF EXPRESSION AND FUTURE POTENTIAL- CONDUCTED AS A TOOL OF MINI RESEARCH PROJECT

Carcinoma of prostate is the commonest type of cancer found in males of developed countries and is responsible for large number of cancer related deaths and signicant morbidity .Gleason’s grade and PSA level play pivotal role in decision making in the management of patients with prostate cancer. By modulating various aspects of tumour progression, Galectin 3 is emerging as a potential guardian of tumour microenvironment and studies indicate that it has important regulatory role in pathogenesis and progression of prostate cancer. An observational cross sectional study was undertaken in the department of pathology of a tertiary care hospital in East India, of 6 months duration. Twenty nine samples diagnosed as acinar adenocarcinoma of prostate were taken by systematic random sampling as per the inclusion-exclusion criteria from the received specimens in the department and immuno-histochemical examination was done on the selected samples using monoclonal antibody against Galectin3 after obtaining thin sections from formalin xed parafn embedded blocks and retrieval of antigen. The data was interpreted by light microscopy using a semi-quantitative method with respect to prexed parameters and statistical analysis was done using SPSS version 25. Based on the prexed cut off, 20.7% of total cases have shown positive expression of galectin3. Mainly the tumours with lower Gleason’s grade have shown positive expression of this marker (62.5% of grade group 1 and 16.6% of grade group 2). None of the cases belonging to grade group 3, 4 or 5 have shown even minimal positivity. Positive expression of galectin3 appeared to decrease with progression of Gleason’s grade and this association was found to be statistically signicant. However, no signicant association has been found between expression of this marker and percentage of the positive cores or the degree of maximum linear positivity.</jats:p

NUMBER THEORY AND THEIR APPLICATION IN COMPUTER SCIENCE AND CRYPTOGRAPHY

Here we have briefly discussed the various applications of number theory in the fields of Computation with special emphasis on Encryption algorithms. We have laid special emphasis on prime numbers and briefly touched upon their importance in modern day Cryptography , especially in RSA Encryption which is the most widely used encryption technique nowadays.</jats:p

Author Correction: Identification, characterization and control of a sequence variant in monoclonal antibody drug product: a case study

An amendment to this paper has been published and can be accessed via a link at the top of the paper.</jats:p

Identification, characterization and control of a sequence variant in monoclonal antibody drug product: a case study

AbstractSequence variants (SV) in protein bio therapeutics can be categorized as unwanted impurities and may raise serious concerns in efficacy and safety of the product. Early detection of specific sequence modifications, that can result in altered physicochemical and or biological properties, is therefore desirable in product manufacturing. Because of their low abundance, and finite resolving power of conventional analytical techniques, they are often overlooked in early drug development. Here, we present a case study where trace amount of a sequence variant is identified in a monoclonal antibody (mAb) based therapeutic protein by LC–MS/MS and the structural and functional features of the SV containing mAb is assessed using appropriate analytical techniques. Further, a very sensitive selected reaction monitoring (SRM) technique is developed to quantify the SV which revealed both prominent and inconspicuous nature of the variant in process chromatography. We present the extensive characterization of a sequence variant in protein biopharmaceutical and first report on control of sequence variants to &lt; 0.05% in final drug product by utilizing SRM based mass spectrometry method during the purification steps.</jats:p