Reviewerly/exHarmony huggingface dataset:
Influence of Data Quality and Quantity on Assamese-Bodo Neural Machine Translation

Influence of data quality and data quantity on Assamese-Bodo neural machine translation model training has been investigated in the current work. A series of experiments with progressively augmented dataset in terms of quality and quantity has shown that although both the parameters- quality and quantity are critical for the performance of a neural machine translation system, data alone can not contribute to the performance enhancement, rather smaller quantity of quality data augmentation could contribute significant improvement in the performance metrics. The experiments have been performed with OpenNMT-py system, and BLEU score has been used for recording performance and analysis. Domain specific BLEU as well as overall BLEU on a test dataset of 500 parallel Assamese-Bodo sentence pairs have been recorded, and the results and analysis are presented with tabular as well as graphical representations.
Emerging Trends in Chatbot Development : A Recent Survey of Design, Development and Deployment

Recent years have seen a considerable breakthrough in chatbot systems, revolutionising human-machine interactions and enabling seamless communication across multiple areas. They have developed into potent conversational agents that are altering how people engage with technology. As the field of chatbot research continues to evolve rapidly, this paper presents a comprehensive review of recent research papers, aiming to identify and analyze the latest trends, innovations, and proposals in the development and deployment of chatbot systems.
Hilarious or Hidden? Detecting Sarcasm in Hinglish Tweets using BERT-GRU

Sarcasm is the manner of usage of words that contradict the actual context of a sentence. Sarcasm detection in multilingual contexts poses unique challenges due to the complex interplay of languages and cultural nuances. Inside this research paper, we propose a novel approach for sarcasm detection in Hinglish, a code-mixed language blend of Hindi and English. Our approach combines the power of BERT (Bidirectional Encoder Representations from Transformers) and GRU (Gated Recurrent Unit) neural networks to capture the contextual and sequential information necessary for accurate sarcasm detection. We evaluate our approach on a code-mixed dataset, comparing it with several baseline models and achieving remarkable results. The proposed BERT-GRU model achieves an impressive accuracy, precision, recall, and F1-score of 0.96, bespeaking its effectiveness in sarcasm detection in Hinglish code-mixed text.
Design and Development of an Interactive System for the Disabled Person

Technology is focused on making life comfortable for humans nowadays. This comfort should be shared and enjoyed by a variety of people at large. The simple interfaces and interactive applications make various things easy and attractive for us. This technological advancement should not only aid general people, but it should also be focused on aiding a special group of people who may or may not be able to use various technological resources available. Thus, we propose this application called ‘Differences’ to celebrate the various groups of people and help ease their lives by providing necessary features to the people according to their specific needs.
Relevant Feature Extraction to Enhance Aspect-Based Sentiment Classification Using Dependency and Bi-Tagged Features

A growing demand for gleaning insights is observed from a large amount of user-generated content available on the internet. The lack of phrases and the semantic ambiguity of single words both have a negative impact on the reliability of sentiment analysis, and this is particularly true when considering the context of brief texts shared on social media. The proposed research work investigates the contextual knowledge usefulness for relevant feature extraction based on dependency and bi-tagged features (DABTF). The prominent features extracted using Information Gain (IG) value improved the classification accuracy. Based on accuracy, the comparison shows Support Vector Machine (SVM) classifier gives superior performance over Naive Bayes (NB) classifier with value 79.67% for the proposed method. The improvement can be observed in the aspect category classification task for food and service categories with F-measures 92.34% and 89.78% respectively.
Evaluating Bert and GPT-2 Models for Personalised Linkedin Post Recommendation

Social networking platforms have become essential tools for individuals and organisations to connect, communicate, and collaborate in today’s digital age. LinkedIn is a professional social networking platform facilitating career development and job searching. LinkedIn’s traditional post recommendation system limits users to seeing only those posts that have been engaged upon by their following or first-degree connections, limiting the user’s perspective. The proposed system leverages the content posted by the user to provide personalised content delivery on LinkedIn, potentially enhancing user engagement and satisfaction. This research study proposes and examines the performance of three content-based recommender models developed with Machine Learning (ML), Generative Pre-Trained Transformer (GPT-2), and Bidirectional Encoder Representations from Transformers (BERT). In terms of capturing the similarity between user-generated and recommended posts, BERT outperformed the other models, achieving the highest similarity score of 97.13%, compared to GPT-2 (96.27%) and basic ML (95.69%).
Enhancing Security and Confidentiality using Trust Based Encryption (DHPKey) in Cloud Computing

Cloud computing has gained widespread adoption in enterprises due to its on-demand service, broad network access, and elasticity. However, security issues make it difficult to use these functions, especially when sending sensitive data through a cloud platform. A secure communication route is necessary to handle this. In order to improve security and lessen threats, the Diffie-Hellman key exchange method is suggested as a workable solution in this research. This technique is used in a cloud-based architecture that is specifically designed for engineering education to increase the security of user data. Multiple security goals, including secrecy, data integrity, and individualization, are successfully met by the design. Encryption, a technique to conceal private or sensitive information within seemingly ordinary data, is employed using the proposed novel method called DHPKey. It combines the benefits of public key techniques with the exchange of a private encryption key. Users can send text as a secret message and encrypt it using a key or password. Even if a hacker gains access to the encrypted text, decryption without the key is infeasible. The recipient, however, can decrypt the hidden text using the provided key. This approach ensures secure transmission of secret messages, safeguarding them from external interference. Publicly shared ciphertext remains incomprehensible to unauthorized users, ensuring that only the intended recipient can decrypt it. The system incorporates an online database for secure storage of all relevant information.
Prohibited Object Detection using YOLOv7 on X-Ray Images of Airport Luggages (OPIXray)

Ensuring airport security and reducing risks for passengers safe travels is crucial. Conventional security inspection methods involve manual inspection of passengers luggage using X-ray scanners to identify any prohibited items. However, this manual process is prone to human error and can be time-consuming. To address these challenges, there is a need for automated security inspection systems that can enhance efficiency and accuracy. Deep learning techniques, particularly in the field of computer vision, offer potential solutions for autonomous security inspection. One such model is YOLO (You Only Look Once), a real-time object detection system capable of identifying multiple objects in images or video streams. This makes YOLO an ideal choice for airport security applications that require both speed and accuracy. This paper proposes a method for the automatic detection of prohibited objects in X-ray images, aiming to identify potential threats such as weapons, explosives, and other prohibited items in real-time. The proposed system was evaluated by conducting extensive tests on the publicly available benchmark dataset known as OPIXray. This dataset consists of labeled X-ray images that contain prohibited objects. The key component of our method involves the utilization of the YOLOv7 network, which was trained on a subset of the OPIXray dataset. The study demonstrates the effectiveness of this approach in detecting and recognizing prohibited items in OPIXray image datasets. The proposed methodology achieved a precision of 0.73, recall of 0.76, mAP@0.5 of 0.491, and mAP@0.5:0.9 of 0.397 on the OPIXray dataset. The experiments demonstrate the model's high accuracy and speed, thereby indicating its suitability for real-world airport security applications. Comparisons with other methods show that our model outperforms in terms of accuracy and speed. This paper presents a high-accuracy method for real-time airport security applications utilizing the YOLOv7 network.
From Bullshit to Bull’s Eye - story of persuasive argument winning us a big pie

Argument persuasiveness applications are becoming increasingly popular in today's digital age. These applications are designed to assist users in developing and delivering effective arguments, with the aim of persuading others to their point of view. The purpose of this research is to explore the applicability and effectiveness of argument persuasiveness prediction model. Specifically, this study aims to predict the argument's ability to influence the opinions of others using sentiment analysis on various argument components.
Mitosis Detection In Breast Histopathology Image Using Ensemble Features Fed CNN Model

Breast cancer is acknowledged as the second leading cause of women’s cancer-related deaths. Quantifying mitosis in pathology sections can provide invaluable insight into both the prognosis and tumor aggressiveness. The task of detecting mitotic cells in histopathology images, which is crucial for predicting the grade and aggressiveness of breast cancer, poses a demanding and time-consuming challenge for pathologists. In this work, we establish an automated approach for detecting breast cancer based on the identification of mitotic counts from histopathology images. In CNN approaches, the generation of features is predominantly unsupervised, and handcrafted features are aimed at modeling domain-relevant attributes. Consequently, a hybrid feature dataset is constructed by incorporating handcrafted features. This dataset can be reorganized and utilized as input for a CNN architecture to classify feature values as either mitotic or non-mitotic. Subsequently, various data combinations are employed to train the CNN model for classification, and the most accurate feature set is chosen to optimize prediction accuracy. In our study, the shape feature has an F-score of 0.87.
Multi-Class classification of Different Cancer Types Using CNN

Cancer is known as emperor of all maladies. It's a group of disease known to be fatal for life if not detected on time. The disease originates in one cell and spreads slowly. Over a period, the cancer cells affect various organs leading to death. It is possible to control and treat the disease in its early stages. The disease detection based on histopathological images provides an advantage for early detection, as the affected cells can be differentiated from the normal cells from their images. Various artificial intelligence algorithms can be suitably implemented in this regard for machine-based detection of cancerous cells and tissues. In this project, Multiclass classification is done using Convolutional Neural Networks for the prediction of various cancer from their histopathological images. A Graphical User Interface was designed to display the outcome in terms of the cancer type from the given image input.
Spam E-mail Classification using ML, Angular, and Flask

Technical advancements are resulting in the development of tactics, that when used by the wrong people, can make a huge loss to society. Spammers nowadays use emails to trick common people, because of the increase in the use of emails to communicate important information. The Unwanted mail that spammers send for their benefit is termed spam mail. The increase in the cases of spam and the loss associated with it threatens people. To counter these attacks some filtering mechanism should be introduced so that the emails can be filtered into two parts that are normal mail and spam mail. This paper tries to solve this problem by using several Machine Learning algorithms, like Random Forest, AdaBoost, Baggage boosting XGBoosting, Decision Tree, Extra Tree Classification algorithm, and Support Vector Machine. The final result depends on the result of each algorithm, and hence greater accuracy to the solution of the problem, the concept of majority voting is used for evaluating the final result. The project deploys the ensembled model in the Flask server to provide the services through APIs to other developers. The user interface is created to provide the user with an interface where they can test their emails. This UI uses these APIs for spam prediction services.
Performance Evaluation of Video-to-Video Synthesis GAN models on Cityscapes Dataset

Video synthesis and prediction are essential tasks in computer vision, finding diverse applications in areas such as video editing, virtual reality, and autonomous driving. This paper presents a comparative analysis of three state-of-the-art video synthesis models: Vid2Vid, Fast Vid2Vid, and Dynamic Multi-scale Voxel Flow Network (DMVFN). Vid2Vid and Fast Vid2Vid are generative models that specialize in synthesizing realistic videos based on semantic segmentation maps. In contrast, DMVFN addresses video synthesis using a voxel-based representation and adopts a multi-scale strategy, generating voxel grids at different resolutions to capture both global and local motion information. In result analysis of video synthesis models, Vid2Vid generator and discriminator generate visually captivating videos with accurate object shapes and colors, with accuracy 84.76% and 85.25%, respectively. Whereas, Fast Vid2Vid has achieved a remarkable speed while maintaining a satisfactory quality level with generator and discriminator accuracy of 88.82% and 87.68% together. Finally, DMVFN excels at capturing complex motion patterns and preserving temporal coherence, so it has achieved 86.64% generator and 86.96% discriminator accuracy, although it may have limitations in generating subtle details. Overall, this comparative analysis provides valuable insight into the strengths and limitations of each model, assisting researchers and practitioners in selecting appropriate video synthesis techniques for specific applications.
Implementation of Convolutional Neural Networks on FPGA for Object Detection

Object detection is a primary task in computer vision applications. Convolutional neural networks (CNN) have emerged as potential technique and aggressively using in object detection and other computer vision applications. However convolutional neural networks (CNN) are more complex and higher computationally intensive and requires more hardware resources. To address this challenge, field programmable gate array (FPGA) offers significant advantages in terms of parallelism and hardware acceleration which makes them more suitable for accelerating CNN algorithms. This work implements a you-only-look-once (YOLO)v2 CNN on Xilinx zynq-7000 soc FPGA and the results show the increase in performance with reduced processing time and less power consumption compared to CPU based system.
Performance Evaluation of English to Bodo Neural Machine Translation System with Varying Model Architecture and Vocabulary Size

This paper is about a work done on Neural Machine Translation of English-Bodo language pair using deep learning technique. Bodo is a language of northeastern part of India particularly in the state of Assam. The experiments are performed using English-Bodo parallel data collected from open sources, as well as created inhouse using expert linguists. The dataset was subjected to data filtering and cleaning. IndicNLP library and Mosesdecoder are used for tokenization for Bodo and English respectively. After tokenization, Byte Pair Encoding (BPE) technique was used for subword tokenization. Two transformer encoder and decoder models are built using different architecture. OpenNMT-py framework is used for building our models. Experiments have been performed on the two models using two different vocabularies of 8000 and 16000 sizes. Highest BLEU score of 10.43 was achieved on the testset from training Model 2 with 8000 vocabulary. The details of the results are analysed against different models.
Research on the Effect of Blockchain Enabling Finance-Transportation and Warehouse Financing Benefits-Based on System Dynamics

Based on the analysis of the basic operation mode of finance-transportation and warehouse (FTW) business and the application of blockchain technology in the design of FTW, this paper constructs an FTW financing system dynamics (SD) model composed of financing enterprise, third-party logistics (3PL) and fmancial institution. Each participant’s change of benefit level is simulated under three policy scenarios, i.e., no financing, FTW financing and FTW financing based on blockchain technology. The simulation results show that: FTW financing based on blockchain technology can increase the income level of all participants of FTW business, and alleviate the operating dilemma of small and medium-sized enterprises (SMEs) with capital constraints caused by inventory occupying funds, which play a positive role in improving the overall benefit of supply chain.
Cybersecurity Maturity Model to Prevent Cyberattacks on Web Applications Based on ISO 27032 and NIST

During the last years, the number of cyberattacks, especially on web applications, has been increasing. Cyber agents target both large and small companies, which is why it is necessary for organizations to have a tool to help them prevent cyberattacks on their web applications such as a cybersecurity maturity model. The proposed model was built considering frameworks and methodologies such as those provided by NIST and ISO, and it defines four levels of cybersecurity maturity: initial, defined, established, and improved. Additionally, the model includes twelve domains and four categories to be considered as a multidimensional model. The proposed model stands out for incorporating two quality standards, the CSF of NIST and the ISO 27032 standard, and being available as part of a technological solution, a web application. This allows anyone to use the model without requiring assistance from the development team, with a track record of assessments and instant results that include recommendations for each domain on how to improve the maturity level and an assessment history. As a result of the validation, fifteen experts participated in the process where they evaluated a web application of their organization and answered a Google questionnaire. The responses were positive, demonstrating that the model fulfills its purpose of being a useful tool for organizations, enabling a quick and automated evaluation of security in their web applications. This contributes to the prevention of cyberattacks and the protection of the sensitive data of its users.
Robust Preliminary Estimation System for Solar Radiation

Solar energy is increasing in popularity due to worldwide requirements for energy, inexpensive production, and environmental sustainability. Plenty of individuals feel inclined to invest in this industry. As a result, precise forecasting of Long-term effectiveness, energy management, and economic concerns all depend on sun radiation. Based on inputs such as air temperature, relative humidity, wind speed, wind direction in degrees north counted clockwise, day duration, and air pressure, this study determines worldwide horizontal radiation. Based on these requirements, the inquiry makes use of the abilities of several machine-learning algorithms to estimate solar radiation. Comparing the models' R2 (coefficient of determination), the XG boost regressor model, which forecasts solar radiation in Pakistan using metrological data, is chosen as the best model. The model retains its accuracy even when the parameters for wind direction and gust speed are removed. When applying algorithms based on machine learning to make estimations, assessment helps. The article is part of a larger scientific effort to anticipate solar radiation with greater simplicity and precision.
Exploring Horizontal Scaling Solutions to Enhance CryptDB Performance under High Concurrency on AWS

This study presents an investigation into the performance of CryptDB, a database system providing practical and provable confidentiality in the face of an adversary who captures all stored data under different cloud-based architectures. The research aims to determine how different configurations impact the scalability and efficiency of CryptDB. The cloud architectures examined include an encrypted environment with no scaling (EENS), an encrypted environment with scaling (EEWS), and an unencrypted data (UE) architecture. A load testing tool, k6, is used to simulate various concurrent user scenarios. The results demonstrate that the proposed EEWS architecture can reduce latency by up to 50%, though it also highlights CryptDB’s limitations, including poor CPU utilization and inadequate documentation. Recommendations are provided for future enterprise projects considering CryptDB. Further research can expand upon this work by considering other alternative tools and their performance under high concurrency.
Fall Detection System using a single Accelerometer through Machine Learning

Falls pose a critical health risk, often resulting in severe injuries or fatalities and profoundly affecting individuals' quality of life. This study introduces an innovative machine learning-based fall detection system that utilizes a single low-cost ADXL accelerometer sensor to capture body acceleration signals during daily activities. By combining data from three axial accelerations into a Sum Magnitude Vector (SMV) and extracting five distinctive features from the time and frequency domain, this system achieves a remarkable accuracy rate of 99.3% when fed into a Support Vector Machine (SVM) classifier, as confirmed through rigorous comparisons with other classifiers. This research presents a promising solution to minimize the consequences of falls, enhancing the safety and well-being of individuals at risk concisely and cost-effectively.
Simulación del sistema solar fotovoltaico de la universidad Autónoma de Occidente

In recent years, artificial intelligence has focused on addressing various issues related to global technologies and needs. Its contributions in areas such as computer vision, sequential signal processing, natural language processing and system optimization using artificial intelligence-based algorithms stand out. This article presents the simulation of the solar photovoltaic system of the Universidad Autónoma de Occidente, covering the two stages of construction. In addition, a storage system connected to each set of solar panels, linked to the inverter, is introduced. The simulation allows an analysis of the most relevant variables for the system: solar radiation, ambient temperature, voltage generated and current generated.