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A real-time AI software solution for a holistic approach to recognizing Indian Sign Language (ISL) where elements of ISL such as hand shape, facial expression, orientation, movement etc. are analyzed, recognized, and converted into written text. Sentences are formed by analyzing each sign one by one and overlapping detections are ignored. It is a software solution that a user can run on their system without installing any dependencies. We also use emotion detection to understand what a person is trying to say as any human being will have emotions while they convey their message. The model is also trained with an ideal state where if no signs are being shown, that is if there are no hand movements, no sign is predicted.

Mediapipe, LSTM, CV2, Indian Sign Language, DeepFace, PyInstaller, Keras, CNN

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Dipankar Mazumder, Upamita Das, Hillal Kumar Roy, Nilava Sarkar, Abhishek Kumar Singh, "Holistic Approach of Indian Sign Language Prediction Software with Emotion Detection", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.155-160, 2023.

Multiclass classification using Support Vector Machine (SVM) is an ongoing research issue. SVM is mainly a binary classifier, but for classification efficiency, it is also used for multiclass classification. In multiclass classification, there are two or more classes and classification is not so easy. That’s why many methods are introduced to extend the classification efficiency of SVM. Directed Acyclic Graph Support Vector Machine (DAGSVM), Binary Tree of Support Vector Machine (BTS) and Error Correcting Output Codes (ECOC) methods are more favourable because of their computation efficiency. In the case of DAGSVM there are many improved methods like Decision Directed Acyclic Graph (DDAG), Divide-by-2 (DB2), and Weighted Directed Acyclic Graph of Support Vector Machine (WDAG SVM) have been developed. The BTS-based methods are SVM with Binary Tree Architecture, and Adaptive Binary Tree (ABT). There are many methods related to ECOC like One-Per-Class (OPC), Discriminant Error Correcting Output Codes (DECOC), and Adaptive ECOC. This paper presented a comparative and analytical survey of those methods and introduces a new model which is an improvement over the existing DAGSVM methods. This model uses Gaussian Mixture Model, K-Means Clustering and Naïve Bayes Classifier for data classification. This model can give better results than existing DAGSVM methods.

Multiclass SVM, Directed Acyclic Graph SVM, Binary Tree SVM, Error Correcting Output Codes.

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Sanjib Saha, "A Comparative Study of Popular Multiclass SVM Classification Techniques and Improvement over Directed Acyclic Graph SVM", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.161-168, 2023.

At the forefront of AI innovation, Rai, an advanced voice assistant, leverages the capabilities of prominent Python libraries. This dynamic fusion of technology, encompassing speech recognition, pyttsx3, pywhatkit, wikipedia, pyjokes, os, and webbrowser libraries, opens the door to limitless possibilities. Rai emerges as a versatile companion, reshaping the digital experience through seamless voice interactions that transcend conventional interfaces. Its diverse array of functions, from orchestrating music playback to extracting information from the internet and providing humor through pyjokes, showcases its versatility. Rai`s AI-powered voice interface transforms user interactions, making task execution efficient and empowering. Rai stands as a game-changer, elevating productivity and digital engagement while harmonizing technology with human expression.

Voice assistant, Technology, Human communication, Task execution

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Arpan Kumar Chall, Anapeksha Das, Asmi Mondal, Radhakrishna Jana, "Wellness Management Guided by Voice Input, Featuring an Intelligent AI Health Assistant Named: Raie", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.168-176, 2023.

Emotional responses play a crucial role in daily social interactions, enabling us to perceive and understand others’ moods and feelings. The field of emotion detection and recognition is rapidly evolving, with Speech Emotion Recognition (SER) emerging as a prominent research area. SER involves the analysis and identification of human emotions through speech patterns, offering significant potential applications in human-computer interaction, healthcare, and education. Current systems for emotion recognition from speech signals employ a variety of techniques, including natural language processing, signal processing, and machine learning. These techniques extract relevant features from speech signals and classify them into different emotional categories. Given the rich characteristics of speech, it serves as an excellent resource for computational linguistics. While previous studies have proposed various methods for speech emotion classification, there is a pressing need to enhance the effectiveness of voice-based emotion identification. This is primarily due to the limited knowledge on the fundamental temporal link of the speech waveform. This paper aims to advance speech emotion recognition by uncovering valuable insights through the utilization of signal processing and feature extraction techniques.

Emotional responses, Speech Emotion Recognition (SER), Human-computer interaction, Feature extraction, Natural language processing, Machine learning.

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[20] Ancilin, J., and A. Milton. "Improved speech emotion recognition with Mel frequency magnitude coefficient." Applied Acoustics 179 : 108046, 2021.
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Divyansh Kumar, Vatsal Kumar Sharma, Avni Chauhan, Gungun Singh, Gurwinder Singh, "Improving Speech Emotion Recognition using Signal Processing and Feature Extraction Techniques", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.177-183, 2023.

Cluster analysis, in unsupervised learning, divides similar data into groups or clusters that are meaningful and useful. Due to good performance in clustering on massive data sets K-Means clustering is feasible in multiple areas of science and technology. The clustering algorithms may face problems of empty clusters and incomplete data. This empty cluster problem is caused by bad initialization of the center point and this may route to signifying performance degradation. In this theme, the K-Means clustering algorithm is revisited from the probabilistic viewpoint and reformed by the similarity among the K-Means and finite Gaussian Mixture Model (GMM). The initial centroids or current best estimate for the parameters are calculated from the list of all data, known and unknown. Therefore, any two or more primal centroids may not be equal or not very close to each other and data will be assigned to the appropriate clusters with closely fair centroids. The newly proposed modified K-Means using GMM of the Expectation Maximization approach efficiently eliminate the empty cluster and incomplete data problems.

Unsupervised Learning, Clustering Analysis, K-Means, Expectation Maximization, Gaussian Mixture Model

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[23] Datta, R. P., and Sanjib Saha. "Applying rule-based classification techniques to medical databases: an empirical study." International Journal of Business Intelligence and Systems Engineering 1.1: pp.32-48, 2016.
[24] Das, Subhankar, and Sanjib Saha. "Data mining and soft computing using support vector machine: A survey." International Journal of Computer Applications 77.14, 2013.
[25] Saha, Sanjib, and Debashis Nandi. "Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison." Int. J. Comput. Appl 129.7: pp.36-41, 2015.
[26] Saha, Sanjib. "Non-rigid Registration of De-noised Ultrasound Breast Tumors in Image Guided Breast-Conserving Surgery." Intelligent Systems and Human Machine Collaboration. Springer, Singapore, pp.191-206, 2023.
[27] Saha, Sanjib, et al. "ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images." Biomedical Signal Processing and Control 85: 104974, 2023.

Sanjib Saha, "Prevention of Empty Clusters and Incomplete Data Problems using Modified K-Means and Gaussian Mixture Model", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.184-189, 2023.

Alzheimer`s disease (AD) is a perpetual neurological disorder primarily affecting the brain leading to cognitive deterioration, behavioral problems, and memory loss. Alzheimer’s disease which poses a significant danger to individuals worldwide in accordance with the World Health Organization (WHO). According to a recent study, by the year 2060, 70% of the population would have this condition. With a prevalence rate of 60 to 80 percent among dementia cases globally, Alzheimer`s disease emerges as the leading etiology. Researchers are diligently working on the development of advanced machine learning models to improve the accuracy of skull stripping, specifically for separating neural tissues from non-neural tissue in magnetic resonance imaging (MRI) scans and identifying affected patients. In this paper, we unveil a fresh perspective of modified 3D-UNet architecture for precise brain segmentation and 3D-CNN architecture for classification. We argue for a volumetric analysis of the whole brain instead of localization and context information-based approaches for disease classification. As the dataset possesses the time-series like nature, utilization of the long short-term memory-based LSTM architecture has been utilized for medical analysis using MRI data from multiple regular patients. It enhances disease diagnosis & treatment effectiveness. The proposed approach demonstrates segmentation accuracy of 97% and classification accuracy of 95%. These findings enlighten the potential of LSTM-based analysis for neuro-degenerative diseases like-AD.

Brain Segmentation, 3D U-Net, Disease Classification, 3D CNN, Alzheimer’s disease, MRI, LSTM

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Suprava Saha, Deepika Das, Aditya Kumar Singh, Sabbir Reza Tarafdar, Tushnik Sarkar, "Neuro-degenerative disease Identification using MRI 3D-Convolution Method", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.190-196, 2023.

In this model, over a predetermined planning period, we study the inventory replenishment strategy for a depreciating good with an exponential time demand function. To reduce the average system cost, the amount of reorders, the gap between reorders, and the gaps between shortages within a given time frame are all estimated. How the approach works is demonstrated by one numerical example. Considering its sensitivity, the significance of the various variables in this model is assessed.

Deterioration, Exponential Demand ,Shortages

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Ayan Chakraborty, "Exponential Time-Dependent Demand (EOQ) Model for Decaying Goods with Shortages", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.197-200, 2023.

Sentiment analysis, commonly referred to as opinion mining, is an important problem in natural language processing that entails figuring out the sentiment represented in a document. Sentiment analysis of Twitter data has drawn a lot of attention as a result of the social media platforms` rapid expansion. Using logistic regression, a well-liked machine learning approach for binary classification applications, this research suggests a sentiment analysis system. The system starts by gathering and preprocessing a sizable Twitter dataset with tweets that have been labelled as positive or negative. By eliminating noise, stop-words, and unimportant information, the text data is cleaned. The techniques of tokenization and vectorization are used to represent the text in a numerical format appropriate for logistic regression. A suitable optimization approach is used to estimate the model parameters as the logistic regression model is trained on the labelled dataset. Cross-validation and performance indicators including accuracy, precision, recall, and F1-score are used to evaluate models. The system`s goal for sentiment analysis jobs is high accuracy and reliable generalization.

Sentiment analysis, Opinion mining, Natural language processing, Twitter data, Logistic regression.

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[15] A. Kumar and G. Garg, “Sentiment analysis of multimodal twitter data,” Multimedia Tools and Applications, vol. 78, pp. 24 103–24 119, 2019.
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[17] K. H. Manguri, R. N. Ramadhan, and P. R. M. Amin, “Twitter sentiment analysis on worldwide covid-19 outbreaks,” Kurdistan Journal of Applied Research, pp. 54–65, 2020.
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[19] P. Chauhan, N. Sharma, and G. Sikka, “The emergence of social media data and sentiment analysis in election prediction,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 2601–2627, 2021.
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Kavinder Singh, Syed Mehdi Abbas Razavi, Sneh Sagar Subedi, Akshay Kumar, Gurwinder Singh, "Improving Generalization in Sentiment Analysis of Twitter Data with Logistic Regression Model", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.201-207, 2023.

With the aid of natural language processing, this study seeks to develop a web application that will promote, protect, and highlight India`s literary heritage as well as the value of its languages. The application offers an interactive user experience that is visually beautiful and welcoming. It features a database exhibiting the many Indian languages, as well as information on their background, cultural importance, and important literary productions. Users can browse language-specific areas, use interactive maps to navigate, access language study tools, join in forums tailored to their language, and take part in virtual literary events. Overall, the website acts as a thorough platform for celebrating and learning about India`s rich literary heritage and linguistic variety.

Heritage literature, language, web application, natural language processing, css, html

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Amrut Ranjan Jena, Mohit Kumar Sinha, Yash Raj, Rikesh Raj, Shivani Kumari, Alka Singh, Nitish Kumar, "Promotion of Indian Languages Literature in Web by applying Natural Language Processing", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.208-213, 2023.

One of the most fruitful forms of media is music since it can evaluate strong emotions and marshal listeners with subliminal instructions. It manipulates our feelings, which in turn affects how we feel. Books, movies, and television are a few other ways to communicate, but music communicates its message in just a few brief seconds. It can encourage us and help us when we are down. We frequently experience a mood when listening to depressing music. We experience happiness when we listen to music. Many Internet businesses have looked for using sentiment analysis to recommend content that is in keeping with the human emotions that are represented in informal texts posted on social networks. Here we propose a music recommendation methodology.

Collaborative filtering, Content based Filtering, Recommendation System.

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[7]. Allalouf, M., Cohen, A., Sabban, L., Dassa, A., Marciano, S. and Beris, S., “ Music Recommendation System for Old People with Dementia and Other Age-related Conditions” ,DOI: 10.5220/0008959304290437 In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 429-437 ISBN: 978-989-758-398-8; ISSN: 2184-4305 Copyright c 2022 by SCITEPRESS – Science and Technology Publications, Lda.

Samya Das, Souvik Sikdar, Soham Dey, Radha Krishna Jana, "A Survey of Music Recommendation System for old age people", International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.214-220, 2023.