Reviewerly/exHarmony huggingface dataset:
Book Recommendation System Using Hybrid Filtering

In the modern world, where individuals may enter a library or browse online platforms without a specific book in mind. However, every reader has their own unique interests and preferences. With the help of Book Recommendation System we can offer a solution by utilizing algorithms to suggest books based on a reader’s interests. This type of system is commonly employed by online eBook providers, such as Google Play Books. With the aim of reducing the need to search for books and providing personalized recommendations, we propose the development of a website for students, designed to simplify the book selection process and eliminate confusion. By leveraging a user’s previous checkout history and search data, the website can recommend books based on the user’s interests. There are several models available for building such a system, including Content-based recommendation, collaborative filter-based recommendation, and popularity-based recommendation. In this paper, we propose to use a hybrid-based recommendation system, combining content-based filtering and shared filtering. approaches for optimal results.
Integrating AI and Computer Vision for Creating a Virtual Mouse

Nowadays innovation turns into much more available with the assistance of remote innovation. Even though there are numerous innovations still there are few slacks for individuals who are contrastingly abled or individuals who cannot access the mouse. Limited answers had been made to solve this dispute; however, they have limitations like making use of freelance sensors which cost excessive and require excellent computer systems with excessive specifications. To get better from the existing system, The proposed mechanism entitled the computer- generated Mouse for individuals with motor disabilities recommends the digital mouse in fondness to the usage of a regular PC mouse. The prime goal of this paper is to boost the interplay among computer systems and individuals (HCI). The proposed mechanism examines the face and it has been surprisingly skilled via the iBUG-300W dataset which makes the machine an applicable answer for all circumstances. As Machine Learning algorithms are getting eminent to clear up tough and exciting real-global troubles around us. The experimental output recommends that the proposed mechanism has generous overall performance in conjunction with applicable responsiveness.
Performance Comparison of Machine Learning Algorithms in Short Message Service Spam Classification

With the introduction of short message service (SMS) in the second-generation mobiles, we find it being exploited by many companies. They tend to spread unwanted advertisements and offers to the end user. These messages are a huge disturbance to the customers. Often customers find it difficult to receive the desired messages. As technology is advancing many methods have been tested for preventing these spam messages from reaching the user. This has been done with the help of machine learning techniques. Some of the popular machine learning techniques to filter out spam messages from ham are logistic regression, SVM, KNN, Naïve Bayes, Decision Tree and Random Forest. This paper focuses on comparing how well all these algorithms classify the spam messages and also determine their accuracy. Based on the findings Support Vector Machine (SVM) filters the messages the best.
Enhancing KYC Efficiency and Accuracy Through Session Recording and Document Auto-Capture

Know Your Customer (KYC) compliance is a critical requirement for financial institutions to prevent financial crimes and adhere to regulatory standards. However, the manual KYC process can be time-consuming and costly. In recent years, the adoption of digital KYC techniques has shown promise in streamlining the KYC process and reducing costs. In this project, we examine the impact of session recording and document auto-capture on KYC. Our findings suggest that these technologies can significantly reduce the time and effort required for KYC compliance, leading to a decrease in costs for financial institutions. Additionally, we talk about the possible downsides and inadequacies of these technologies and make suggestions for additional studies in this field. Overall, this paper highlights the importance of leveraging emerging technologies to optimize KYC compliance processes and improve efficiency in the financial sector.
Design and development of indoor mobile robot for disabled people

The use of wheelchairs is prevalent worldwide, with an estimated 70 million people relying on them for mobility, according to the World Health Organization. A fact sheet from the World Health Organization says that wheelchairs help people move around with or without assistance who have impairments that make them unable to walk. Wheelchair bound people need to be supervised and assisted by someone to move around. Instead of hiring health care employees to assist the impaired patients, we developed a wheelchair assistant robot. In our system, initially, the user will make a request through a webpage to pick up impaired patients. When the robot reaches the wheelchair location, it docks with the wheelchair and navigates autonomously towards its destination. Once the wheelchair reaches the destination point, the robot undocks from the wheelchair. The robot will be idle at the charging station until it receives the next request from the user. The development of this wheelchair assistant robot can improve the mobility and independence of impaired patients while reducing the need for constant supervision and assistance. This technology has the potential to significantly improve the quality of life for millions of individuals worldwide. A minimum number of Autonomous Assistance robots in the health care sector can manage multiple wheelchairs efficiently. So, with the help of this method, we could reduce the cost of purchases with ease. And this article explains how the robot is developed and subjected to real-time situations with or without a payload and helps us understand the further research findings in wheelchair-based mobility.
Secure Data Transmission using Hybrid Crypto Processor based on AES and HMAC Algorithms

In today's digital age, vulnerabilities in digital platforms have become a major concern, as cyber-attacks can lead to significant financial losses and privacy breaches. Encryption and authentication algorithms, such as AES and HMAC, are commonly used to secure digital platforms. However, running these algorithms in software can burden the CPU, leading to inefficiencies and potential security vulnerabilities. In this proposed paper, the implementation of a crypto-processor based on AES and HMAC to address the inefficiencies of software-based encryption and authentication. Our implementation uses AES for encryption and HMAC for integrity and authentication. By implementing these functions in hardware, we achieve greater efficiency and security, while reducing resource consumption and power usage. Our proposed crypto-processor design provides an efficient and secure solution for digital platform security, improving the overall performance of cryptographic operations. We demonstrate the effectiveness of our design through a comparison with software-based implementations of AES and HMAC, showing that our design provides a significant improvement in terms of throughput, latency, and power consumption. Our research contributes to the ongoing efforts to improve the security and efficiency of digital platforms, helping to protect sensitive data and ensure the privacy of users. Our proposed crypto-processor design provides a cost-effective and scalable solution for securing digital platforms, making it suitable for a wide range of applications in both commercial and government sectors.
Wireless Signal Recognition Based on ResNet-Transformer

In the face of the rapid development of wireless technology in the Internet of Things (IoT), the communication capacity that wireless spectrum can carry is increasing. This has led to wireless signal recognition becoming a key technology for intelligent spectrum management. This paper proposes a deep learning model for wireless signal recognition, called Hybrid net. This model consists of a residual block that extracts local information from the sample and a Transformer block that extracts global information from the sample. These two blocks are added together through a residual unit to form a more complete representation of the feature space. It also avoids the problem of high model complexity and long training time caused by using data in frequency domain format. In order to better simulate actual communication environments, in-phase and quadrature (IQ) and IQ with random phase offset datasets were constructed for training and testing. Experimental results show that the proposed Hybrid net can achieve recognition accuracy of over 80% at low signal to noise ratios (SNRs grater than −6 dB). The Hybrid net even reach 100% at 4 dB. In addition, Hybrid net also demonstrated good generalization performance on different datasets.
An insight into NFTs, Stablecoins and DEXs in Blockchain

For a comprehensive understanding of blockchain’s rising popularity and its potential to replace the current web2, it is crucial to grasp how this technology functions and the skills required by professionals sought after by companies. This paper explores the interplay between three key blockchain applications: Non-Fungible Tokens (NFTs), stablecoins, and decentralized exchanges (DEXs). It highlights their functionalities and demonstrates their combined potential in creating a secure, transparent, and efficient marketplace for digital assets. Additionally, the paper delves into blockchain architecture, popular networks, and cryptocurrencies, showcasing the importance of blockchain in fostering trust, transparency, and security.
Acoustic Word Embedding Model with Transformer Encoder and Multivariate Joint Loss

As a representation of speech information, acoustic word embedding can enable query-by-example keyword search with low-resource speech data. An acoustic word embedding model with Transformer encoder and multivariate joint loss is presented in this article. First of all, on the basis of BLSTM, the model can extract richer information by adding a Transformer encoder, which improves representation ability of the embedding vector. Then, the contrast loss is improved according to the multiple negative samples, and combined with the hinge loss with distance characteristics, the model generates a more compact vector representation and improves the identification of features. Finally, considering the unbalanced characteristics of samples, the anti-focal loss is introduced to form a multivariate joint loss together with the above two losses, so as to make the model performance better. The model has achieved certain results in word recognition tasks and has certain competitiveness compared with other acoustic word models.
Evolutionary Neural Network Based on Quantum Arithmetic Optimization Algorithm for Adaptive Anti-jamming Decision

Due to the broadcasting characteristics of wireless channels, jamming poses a serious threat to wireless communications. In order to prevent jamming from hindering communication, this paper considers the use of evolutionary neural network based on quantum arithmetic optimization algorithm to make adaptive anti-jamming decision. In this paper, we proposed a new adaptive anti-jamming decision-making method based on quantum arithmetic optimization algorithm (QAOA) evolutionary neural network. In order to solve the hyperparameter setting problem of DNN, we designed a quantum arithmetic optimization algorithm (QAOA) based on arithmetic optimization algorithm (AOA) and quantum computing mechanism to optimize the hyperparameters of DNN so that the designed DNN have better convergence and results than the DNN obtained by trial by error under the same number of training. The experimental results show that the QAOA-DNN we designed can quickly select the optimal anti-jamming communication scheme for different communication environments. Compared with other optimization algorithms, the QAOA designed in this paper has faster convergence speed and higher convergence accuracy.
Fog detection and visibility measurement using SVM

Fog which present in the atmosphere due to temperature variance reduces the clear vision of driver or driving applications. Restricted visibility due to fog will affect the accuracy of visualization and it may leads to accident. To overcome this, defogging need to be done in a moving vehicle. We propose a cost-effective fog detection system which can be used in real world. The input images are taken from the camera which is mounted on the dashboard of the car. Density of the fog and visibility distance is calculated using region of interest techniques to provide clear visibility to the driver. In this system, machine learning Algorithm SVM is used to classify the fog and fog-free images. NI Vision Development Software is used for machine vision application for graphical view.
An Efficient Detection and Classification of Acute Lymphoblastic Leukemia using CNN models

One of the major ubiquitous kinds of leukemia is ALL (Acute lymphoblastic leukemia) and potentially lethal hematological malignancy is distinguished by the uncontrollable proliferation of premature lymphocytes in peripheral blood and bone marrow stem. Traditional Acute lymphoblastic leukemia diagnosis, which is done by competent examiners employing microscopic images of a smear of the peripheral blood, is labor-intensive and time-consuming. CNN(Convolutional Neural Networks) is currently the prioritized option for histopathology image processing. Conventional CNN typically requires massive databases for adequate training in order to achieve excellent performance. This paper suggests a prompt, intense CNN architecture to address which concern and achieve better identification of ALL. A unique probability-based parameter is proposed, which has a substantial impact in dexterously hybridizing Vgg16, GoogleNet, and AlexNet while preserving the advantages of each individual approach. Further, the models are trained and validated with various parameters, the algorithms with the best parameters were applied to the test set. Among models, GoogleNet, AlexNet, and VGG-16 achieved 97%, 96.52%, and 98% accuracy, respectively. The model also shows high precision and recall values for both healthy and leukemia-affected WBC. The result shows that the proposed method could indeed aid in the diagnostic test of ALL by inspecting immature leukocytes efficiently.
Alumni Management and Networking System

The Alumni Management and Networking System aims to address the challenges of alumni disconnection and facilitate effective communication and engagement between educational institutions and their alumni. The system provides a platform for institutes to create profiles, connect with fellow alumni and offers opportunities for alumni to contribute to the institution's success through mentorship, volunteering, and other forms of support. This paper reviews existing research on alumni management systems, highlighting their strengths, limitations, and gaps. It evaluates different approaches such as centralized systems, data collection methods, interactive questionnaires, secure messaging, and tag recommendation systems. The proposed Alumni Network Management System incorporates content-based tagging, utilizing a fine-tuned BERT model trained on a dataset of 10,000 Twitter posts. This approach generates relevant tags for new social media posts, enhancing content visibility and discoverability. Additionally, it employs end-to-end encryption for secure messaging, ensuring privacy and confidentiality. The system's implementation has been tested successfully, showcasing its user-friendliness and effectiveness in managing alumni databases. The results indicate that the system provides a convenient platform for alumni to update their information, stay connected, and engage with their alma mater.
Data Monetization Using Smart Contracts

Blockchain technology is poised to change nearly every facet of our digital lives. Blockchain can be said as an immutable, and decentralized database. Data stored in the blockchain cannot be tampered, making it secure. Also, being decentralized, no central entity controls the blockchain, ensuring reliability. So, the data can be stored publicly, such that anyone could read the data. This vast availability of data could enable data scientists to perform various analytics over the large amount of data. This could result in many useful insights in many fields. But, when sensitive data such as healthcare data and reports are to be stored in the blockchain, it could raise several privacy issues. Medical reports or personal information cannot be stored in a way that any one could access them. Thus, this paper suggests a way to store and perform analytics over sensitive data in blockchain. In this paper homomorphic encryption is used to store the sensitive data in blockchain. Computed results from homomorphic encryption on ciphertexts are encrypted. When the encrypted result is decoded, it produces a result that is identical to what would have happened if the operations had been carried out in plaintext. Thus, critical data are encrypted and stored in the blockchain, data analytics are performed over them, without knowing the actual data. Thus, the proposed system provides privacy of the data stored publicly and could also profit the data scientists with access to large amount of real time data directly from the owners of the data.
A Platform to Assist Pathologists in the Cancer Detection Process

Cancer is a complex disease affecting millions worldwide, and early detection and accurate diagnosis are crucial for effective treatment. Biopsy tiles are an essential diagnostic tool in cancer detection and play a significant role in cancer pathology. This research paper proposes the development of a website for multiple cancer detection using biopsy tiles, which would aid pathologists in affirming their findings in cancer pathology. The website would use machine learning algorithms to analyze biopsy tile images and provide a preliminary diagnosis for multiple cancer types. This paper outlines the gradio interface development, which entails using pre-trained neural networks like InceptionV3, ResNet50, Alex Net, and VGG16. The interface also helps the pathologists ensure how the model learns a specific image and can modify it according to their own purpose. Having given access to the code to the pathologists, they can even modify or integrate new data into the model. The proposed interface has the potential to improve the accuracy and efficiency of cancer diagnosis, which could ultimately lead to better patient outcomes.
Enhanced Breast Cancer Detection in Multiple Imaging Modalities using Deep Learning

According to statistics, there are around 2.1 million new cases of breast cancer each year, making it the most common kind of cancer among women worldwide. Approximately 15% of female cancer fatalities in 2018 were believed to be caused by breast cancer, which claimed 627,000 lives. For diagnosis and prompt treatment, it is crucial to have an accurate analysis of medical pictures. Breast cancer has been classified using different methods. Recent years have seen an increase in interest in the use of deep learning algorithms for breast cancer categorization due to their high accuracy in prediction compared to conventional methods. In this study, three different datasets that were screened using ultrasound, mammography, and histopathology procedures were employed. A customized and fully connected CNN with data augmentation is built to have enhanced accuracy. The features were trained after being retrieved. Inception Net transfer learning model was used to further boost accuracy. As a result, the model’s accuracy for all three datasets was raised to more than 94%.
IoT Device Security for Smart Card Fraud Detection for Credit Cards

Credit card theft is on the upswing as technology advances. Also, it may be appealing that as global communication advances, economic fraud is sharply rising. Millions of dollars are lost every day due to these frauds, according to records. master card fraud states to the somatic forfeiture of the master card or defeat of profound master card information. For detection, a variety of machine learning methods are frequently utilized. This study presents a few of the algorithms that will be used to catalogue connections as faithful or fraudulent credit cards. The study used a dataset for fraud detection. Sorting and searching were utilized for oversampling because the dataset was quite unbalanced. NFC technology is decisive for user re-authentication as integrates the Internet of Things for double transaction verification to reduce fraudulent transactions, Random Forest, Bayesian Network, and Multilayer Perception were the strategies adopted in the experiment, including feature selection and dataset division into training and test data. Results show that every technique is regularly and reliably used to spot master card fraud.
Sign Language Detection Using Faster RCNN Resnet

Sign language recognition unites society with people who cannot hear. Sign Language involves a series of hand gestures each with their own meaning and can vary with region and time. A system is required to overcome this barrier between the disabled people and others. Sign Language Recognition is classified in two different ways, they are image and sensor based. Image-based approaches for sign language recognition involve capturing multiple images of the signer performing a sign using one or more cameras, and then analyzing and processing these images to identify and recognize the sign being performed but are not perfectly accurate. In the sensor-based method, instrument gloves assembled with sensors are used to monitor the joints of the hands. These are very accurate but very costly and not easily accessible. In this project an image-based model is used to detect the signs made by a person. Steps involved are: Capturing an Image, preprocessing the image which includes resizing it to 640X640 size, feature extraction, sign classification. The model being used is Faster-RCNN- Resnet, a pretrained model from TensorFlow model zoo.
E-auction using Blockchain Mechanism

Online auction system has experienced significant growth in recent years due to its convenience, accessibility, and wider reach. The COVID-19 pandemic has also accelerated the use of online auctions, as more buyers and sellers turn to digital platforms. But online auction systems have the vulnerability of fraudulent transactions. Hence, by incorporating blockchain technology with the exiting online auction system, a new electronic auction system can be developed to enhance the security in bidding operations. As certain blockchains employ smart contracts it is possible to automate the validity of all bidding actions. By leveraging a decentralized approach, all auction-related data and transactions are recorded on a blockchain that ensures immutability, transparency, and accountability. MetaMask also provides an additional layer of security, allowing participants to verify transactions before recording the data in blockchain. As security has become a critical aspect of electronic auctions the use of blockchain and smart contracts would be appropriate. By leveraging these technologies, a system can enable global participation in auctions that leads to a significant increase in prices and outcome for both buyers and sellers.