Reviewerly/exHarmony huggingface dataset:
Uncertainty Analysis in Machine Learning

In machine learning, no model achieves 100% accuracy because of their probabilistic nature, and thus they end up providing a probability for false predictions. In B2B applications false predictions have a huge impact on the product. So, it is important to ascertain the incorrect inferences, but based on just probability analysis, we can't tell them with confidence. One of the main reasons to get a false prediction is the presence of outliers in the multivariate dataset. These outliers can unduly influence the results of the analysis. Outliers are data points with deviated statistical properties from normal observations. But the problem here is that the change in statistical properties of one feature does not imply a change in the overall data record. So, to address these issues, we present a method that calculates a score for each record of the dataset, based on feature weights and outlier scores, and using that score, we determine a confidence interval so that it will help us identify false predictions caused by an ML model when it is in production.
A Web-Based Chatbot for Indian Cities: A Comparison of CNN, ANN, and LSTM Models

Digital transformation is necessary to bring an inclusive opportunity for technology and society to meet the needs brought by both environment, circumstances, and events. In this work, we explore the capability of Generative AI in the societal context of smart city resource management with a particular focus on tourist service management. This work attempts to collaborate many services and resources as an information service to cater to the needs of smart city tourism. We demonstrated the potential of AI in achieving this collaborative information service dissemination with the help of an AI-enabled chatbot Web-application. We also discuss the technical elements required for the real-time information management that we envision for future smart city tourism initiatives. Our experimentation shows that LSTM outperforms CNN and ANN in performance.
Blockchain Unleashed: Empowering Information-centric Network Computing in Cloud, Fog, and Edge Services

Blockchain technology has been gaining a lot of attention due to its potential to improve data security, privacy, and decentralization. In this paper, we explore the use of blockchain in cloud, fog, and edge computing services. We analyze the unique characteristics of these services and how blockchain technology can enhance them. One of the benefits of blockchain in cloud computing is that it allows for secure and transparent data sharing among multiple parties. Blockchain can ensure the authenticity and integrity of data with smart contracts, which can automate processes and eliminate the need for intermediaries. This can result in cost savings and increased efficiency. In fog computing, blockchain can be used to enable secure and real-time data processing at the network edge. This can improve latency and reduce bandwidth consumption by processing data closer to the source. Additionally, blockchain-based fog computing can provide better data privacy and security for connected devices. Edge computing can benefit from blockchain by providing a trustless environment for data sharing and storage. Edge devices can act as nodes in a blockchain network, allowing for decentralized control and increased security. This can enable new use cases such as peer-to-peer transactions and autonomous decision making. Overall, blockchain has the potential to empower information-centric network computing in cloud, fog, and edge services by providing increased security, privacy, and efficiency. However, there are still challenges that need to be addressed such as scalability, interoperability, and governance.
Deep Neural Network Architecture for Mobile Device Identification

Attribution of source mobile devices is having a lot of applications in the various domain including image forensics. It aims at identifying the mobile device using which the questioned image has been taken. Mobile Device Identification (MDI) empowers the forensic investigator to fix the device model that has been used for capturing the specified image during the investigation. In this paper, a computationally effective shallow neural network learning approach is proposed for MDI. A noise pattern is present in the mobile cameras sensors which can be very well recognized by CNN architecture. The proposed solution aims at identifying the source mobile device by using its captured image. An accuracy of 98.8% is achieved for 10 model-based experimentation using the vision dataset.
CopulaGAN Boosted Random Forest based Network Intrusion Detection System for Hospital Network Infrastructure

The growing dependence on technology in healthcare has resulted in the creation of sophisticated hospital networks that are highly linked and vulnerable to cyber threats. A reliable Network Intrusion Detection System (NIDS) is required to identify and prevent such cyberattacks. The network intrusion detection is vital for safeguarding hospital networks and guaranteeing data security. The CICIDS2017 dataset contains a comprehensive set of network traffic characteristics for assessing network intrusion detection systems. Besides that, class imbalance is a prevalent difficulty in intrusion detection and it may have a considerable impact on the effectiveness of classification algorithms. The suggested solution employs a Machine Learning (ML) based NIDS for hospital networks that utilizes CopulaGAN (Generative Adversarial Network) to address the challenges due to imbalanced class ratio. The synthetic samples of minority classes were created to balance the dataset and improve detection accuracy. The Random Forest (RF) algorithm is used to discover the most defining features in the dataset and its hyperparameters are tuned to improve classification performance. Overall, the CopulaGAN boosted Random Forest based NIDS described here is a valuable solution for detecting network intrusions in hospital networks.
Indian Currency Recognition for Visually Impaired Individuals

The system described in the research paper is built to recognise various banknotes using computer vision and machine learning methods and to give the user aural feedback through speakers or headphones. The suggested system makes use of a camera module for the Raspberry Pi, the OpenCV library to collect photos of currency notes, and a convolutional neural network (CNN) to classify the notes according to their properties. Because it is built on the Raspberry Pi platform, the system is portable and usable by people with visual impairments. The system's design and development, including the phases of data gathering, preprocessing, training, and testing, are covered in the paper. The technology detects different currencies with high accuracy rates and can give the user feedback in real time Computer vision also has a wide range of other uses, including picture recognition, object detection, face recognition, etc. Applications for machine learning include fraud detection, spam filtering, recommendation systems, and many more. There are numerous more techniques to assist those who are blind, including braille displays and text-to-speech programmers. Many other inexpensive gadgets, such Arduino boards and Beagle Bone Black boards, can be used for comparable tasks.
Comparative analysis of Data Extraction for Qualcomm based android devices

This study focuses on the analysis of data extraction tools for Android devices using Qualcomm processors, with a specific emphasis on UFED Physical Analyzer. The study compares and evaluates the effectiveness of UFED Physical Analyzer alongside other data extraction tools, such as Oxygen FD, XRY, and Cellebrite PA. The assessment includes the speed, ease of use, accuracy, and completeness of the extracted data. The study emphasizes the importance of continuous analysis and improvement of data extraction techniques for Android devices, considering the constantly evolving mobile technology landscape. Moreover, the study also covers physical and logical extractions. By assessing the effectiveness of UFED Physical Analyzer and other tools in both physical and logical extractions, the study provides insights into the advantages and limitations of each technique, facilitating digital forensic investigators and law enforcement agencies in choosing the most appropriate extraction method for their investigations.
Blockchain-Based IoT Applications, Platforms, Systems and Framework

Blockchain is one of the trending technologies in today’s world. All other technologies like IoT, bigdata use blockchain in order to provide efficient process. The main aim of this project is to describe the importance of blockchain with the help of IoT application, platforms and framework. It is acted as distributed based technology which can gain more attention in today’s world. This project explains with different types of method like gateway process and sensor device. By overcoming the existing issues in blockchain, IoT may enable lots of security service that are all explain in detail. Different authors provide some usual information regarding the usage of blockchain in IoT that helps to understand the concept deeply. Blockchain gives better security options and privacy in IoT platform. With the help of all resource and information that are mention in existing process is developed and produce some extra immutable leger in this project [1].
A Novel Approach based on Probability Color Distribution and Color Models to Detect Salient Object

A salient object is the most important object in the scene which attracts to the human visual system. Salient object detection has been attracting a lot of interest, and recently various heuristic computational models have been designed. This paper presents a novel approach consisting of an algorithm based on RGB and HSV color models, and probability color distribution for detecting an appropriate salient object. Firstly, the proposed algorithm removes the background pixels of the image by subtracting the average mean of RGB channels from all the channels. Thus, fewer contributing regions from the channels are removed; still, a few non-salient regions remain. Then, the mean of the RGB component is subtracted from each RGB image respectively to remove less prominent pixels of each component and a most probable salient region is obtained after combining. This image is converted into HSV color. Hue and saturation have a strong representation of the salient object. The mean of HSV is computed to remove the non-salient region. The small mean image of HSV is multiplied by the maximum mean image of RGB to obtain higher probability color regions representing the salient region. Then, the salient region is pipelined to probability color distribution computation for non-salient region removal and finalizing correct detection of the salient object.
Prediction of Global Sea Water Level using Linear Regression and Gradient Descent

A sea level graph was published online in April 2022. "Climate Change: Global Sea Level" seems to be the current name for it. However, it contains little to no anchoring in provable facts. This paper examines a real-world situation. Real worldwide sea level fluctuations range from 0.0 mm/year to +1.0 mm/year on an international scale. The average global sea level has increased by 21 to 24 centimetres (8 to 9 inches) since 1880, according to measurements made from 1880 to 2013. In this study, we are developing models to forecast future seawater levels at the global sea level. In addition, we can observe the significantly rising sea level since the year 2000. The models are developed using JupyterLab Simulation Software, which is combined with the Python programming language to produce a graphic user interface (GUI). The results of the simulation show that the water level of the global sea will keep rising in the years to come. The study’s findings indicate that up until the year 2050, the global sea level will continue to change.
BeautyShop Recommendation System

E-commerce will make up 20.4% of global retail sales by 2023.A promising future for online commerce is offered by India’s rising Internet user population. With the exponential growth of e-commerce and the abundance of products available online, consumers often face the challenge of choosing the right products that meet their specific needs and preferences. Online shopping recommendation systems have emerged as powerful tools to address this issue by providing personalized product recommendations based on user data and preferences.For the purpose of recommendation there are various techniques namely Collaborative filtering, Content-based filtering, Matrix Factorization, Association rule, Hybrid Recommender System, Demographic Filtering, Context Aware filtering, Deep-learning based recommender system, Popularity based filtering, Sequential recommendation etc. To recommend a right product to the user the preferred algorithm is Apriori. The Presented Recommendation system generates recommendation using python, flask framework and MySQL as backend. Client’s happiness is positively impacted by the features of the product recommendation system, thus it’s important to meet client demands based on a survey that prioritizes quality.
AI-powered Chatbot for Improved Customer Feedback Management using NLP

Artificial intelligence (AI) has revolutionized the way businesses operate and make decisions. One of the key areas that AI has made a significant impact on is feedback management. With the help of AI, businesses can now analyze reviews in real-time, uncovering valuable insights that were previously hidden, and drive growth. In this paper, we have presented numerous ways by which we have worked towards analyzing data received by a paper manufacturing and distribution company and maximizing their profits. Here we have described our approach for developing a smart chatbot in extensive detail. With the help of Natural Language Processing, we have been able to classify the customer enquiry comments into 12 unique classes with 91.35% accuracy using Gradient Boosting Classification model. Furthermore, we employed the Random Forest Algorithm, Support Vector Machines, K Nearest Neighbors, and the Decision Tree Algorithm to make predictions based on the text data and have compared their performances.
Utilizing Federated Learning and CNNs for Severity Analysis of Fenugreek Leaf Diseases

To effectively control fenugreek leaf diseases, which represent a severe danger to crop yield, it is necessary to have precise and fast detection systems. This study suggested a unique method for identifying and categorising fenugreek leaf illnesses according to severity levels, combining federated learning with convolutional neural networks (CNN). This study set out to develop a privacy-preserving, decentralized learning model that could assess the severity of fenugreek leaf diseases across various customers' information, enabling more robust and generic predictions. The effectiveness of the local models was evaluated using data from five customers, and the results showed outstanding precision, recall, F1-score, and accuracy values ranging from 79.48% to 97.41%, 82.51% to 97.35%, and 0.95 to 0.99, respectively. Then, a global model with precision, recall, F1-score, and accuracy ranging from 91.14% to 94.05%, 91.70% to 94.49%, 91.25% to 94.19%, and 0.97 to 0.98, respectively, were created using the federated learning method. The macro-average values varied from 91.36% to 94.24% in evaluating the three distinct averaging methods—macro-average, weighted-average, and micro-average—reflecting a dependable and well-rounded performance across all severity levels. The weighted-average figures showed high performance, especially for most courses, and varied from 91.99% to 94.71%. The micro-average statistics, which consider every instance equally, ranged from 91.89% to 94.66%, demonstrating solid performance in every case. As a result, the study showed how federated learning combined with CNN might effectively be used to identify and classify fenugreek leaf diseases, attaining good performance across various measures. This research expands the potential of AI-driven agricultural practices and helps create more efficient and productive farming systems.
Credit Card Fraud Detection and Analysis

In today’s digital world, cybersecurity plays a crucial part. Whenever someone talks about security in the digital world, the main task is to recognize abnormal activity. Whenever someone makes any transaction online, a high percentage of people prefer credit cards. The credit limits we set helps the user make purchases even if they can’t afford the product at the current moment. These features can be misused by cyber attackers.It is necessary for credit card providers to identify fraudulent transactions so that customers are not charged unnecessarily. This paper will provide an approach using machine learning algorithms to detect anomalous transactions.
Tokenization effect on neural machine translation: an experimental investigation for English-Assamese

Tokenization, as a research task, is mostly overlooked when dealing with machine translation as much emphasis is placed on modelling or data enhancement, not to speak for language pairs categorized as low-resourced. The current work takes up this task of an experimental analysis of tokenization on Neural Machine translation (NMT) on a resource-poor language pair: English-Assamese. Four implementations of tokenization available as library are selected for this study: Moses, Nltk, OpenNMT, IndicNLP. The efficiency of these tokenizers in handling the language specialities is also discussed. Both the source and target languages are tokenized with these methods and 12 different NMT models are developed by choosing different combinations of the tokenizers. Perturbations in the evaluation scores have been observed for each combination in both directions (En→As and As→En). It is striking to observe that tokenizing Assamese text with Moses produces the most degrading results. The tokenization effect is again confirmed by testing with the source text tokenized with different tokenizers wherein it was clear that the tokenization schemes followed during model training has to be adhered to produce the best results.
An Efficient Approach to Solve Complex Rook Polynomials in Strategic Games

A rook polynomial in combinatorial mathematics is a generating polynomial of the number of possible arrangements of non-attacking rooks on a checkerboard-like board, where no two rooks may be arranged in the same row or column. The board is any subset of the squares on a m by n rectangular board; we refer to it as the squares where a rook may be placed. There are various formulas like expansion formula and product formula that provides a polynomial related to a given circumstance i.e. for a particular number of rooks in a particular size of checkboard given but for generating functions like n x n arrays to construct rook polynomials for a board that is decomposes into n disjoint sub-boards C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> ,….., C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> and a rook polynomials with R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> , n(x) possible rook movement spaces in a combinatorial way is very tedious task with practical approach.Therefore, in this paper we attempt to automate the rook polynomial and apply them on a practical basis to solve strategic games. Further attempts have been made to formulate an algorithm to paint a picture of solving rook’s polynomial using backtracking and various other strategies like recursion and loops to combine or place the contents of the list such that no similar content will be placed in same row or same column.
Designing a book recommendation system in library using collaborative filtering system

The amount of data has increased, leading to the importance of data technology. The process of extracting information from a broad database is called Data Mining A recommendation system is a data filtering technique that makes suggestions to users in accordance with their requirements and areas of interest. Systems of recommendations are created to assist users in making the best product selections.It is getting harder for library patrons to select the appropriate books as the collection of books grows. A book recommendation system aids users in selecting the top book from a sizable selection. There will be many number of books on a particular subject or topic in university libraries. Students must pick the best book out of all of them. In this project, we'll create a recommendation engine based on the books that students from previous batches who received higher grades recommended. A collaborative filtering technology can be used to accomplish this. The advantage of the system is it reduces data sparsity and cold-start problems as earlier recommendation systems.
Search Engine Using NLP Text Processing Techniques to Extract Most Relevant Search Results

This research proposes the use of a machine learning-driven search engine that employs natural language processing techniques to enhance search results in the face of expanding digital data. To achieve this, the search engine relies on a diverse corpus of relevant text data, which undergoes pre-processing. By extracting features such as word frequency, TF-IDF, or semantic embeddings like Word2Vec or GloVe, the search engine can identify word patterns and relationships, thus improving the accuracy of its language model. The trained model is then evaluated using benchmark or annotated data to identify weaknesses and make improvements. The study emphasizes the potential of optimizing ML-based search engines for specific domains by training algorithms on domain-specific data and incorporating specialized features and knowledge. Overall, this approach offers a promising solution for effectively managing and analyzing the ever-increasing volume of digital data.
Automated Diagnosis of Lymphatic Filariasis: A Robust Approach for Microfilariae Detection using Image Processing and Stacking Classifier

Lymphatic Filariasis (LF) is a debilitating disease that results from bites by disease-carrying vectors. It leads to severe physical deformities and disabilities in affected individuals. Presently, the detection of LF relies on the visual identification of microfilariae parasites in peripheral blood samples, a procedure performed by trained hematologists. Nevertheless, this manual investigation approach poses the potential for inconsistent results. The primary goal of this research paper is to introduce an automated diagnostic approach designed to detect the presence of microfilariae in blood smear samples, with a particular emphasis on identifying cases of Lymphatic Filariasis. The methodology consists of introducing some pre-processing techniques like Color space conversion and the use of a Median filter. Further, the images are segmented to mark the region of interest on microfilariae dropping its background noises. A diverse range of textural features is extracted, serving as input to the stacking classifier, resulting in an impressive accuracy rate of 95%. The achieved accuracy rate provides strong confidence in the viability and applicability of the proposed methodology for real-world diagnostics.