Reviewerly/exHarmony huggingface dataset:
Improving Object Recognition Accuracy through CNN-based Localization Techniques

Convolutional Neural Networks (CNNs) and deep learning are responsible for the quick advancement of computer vision. A crucial task in computer vision is object localization, which involves identifying and finding objects within images. In this study, we propose an object localization method using a TensorFlow CNN model. In the proposed method, the model is trained on a specific dataset of images containing emojis with annotations for object locations and using it to predict the locations of emojis in test images. The performance of the proposed model is evaluated on a validation set, achieving a mean intersection over union (IoU) score of 0.85, indicating high accuracy in detecting emoji locations. The proposed results show that the proposed TensorFlow CNN model can effectively localize emojis in images, which can have practical applications in fields such as social media analysis and emoji-based communication. The proposed method can also be extended to localize multiple emojis in images by modifying the model architecture and training on a larger dataset.
Blockchain based System for Secure Property Registration

The procedure of registering a property involves paying stamp duty, and registering the sales deed for the property you have purchased. Property registration is done at the office of the sub-registrar who has jurisdiction over the area in which you purchased it. Land/property registration is a long process necessitating numerous paper checks, in-person meetings, and cash exchanges, which results in significant delays and is both time- and money-consuming. In India, currently government is practicing the paper-based land registration system which raises significant problems like high time intricacy, centralized authority, physical visit to property site for authentication, high cost of transactions, proneness to human error, frauds involving property documents, lack of communication, third-party participation, ineffectiveness, timing issues and issue with ownership. These issues are getting worse every day; hence the registry procedure system needs to be improved. In this study, to overcome such challenges we will develop a tamper-proof, fraud-resistant, automated Blockchain - based system to create, issue, securely store, share and verify the Land Registration Records. The proposed system will also provide a digital locker all the stakeholders for keeping land record certificates. All the actions taken by different entities involved in the land record registration will be time-stamped, verified by the different nodes, and stored as a block which has a unique hash value, so that neither the hackers/spammers nor the insiders can issue a counterfeit certificate on a later date or modify/tamper the original certificate.
Enhancing Fashion Cloth Image Classification through Hybrid CNN-SVM Modeling:A Multi-Class Study

The classification of fashion cloth images is an important and challenging task in the field of computer vision. In recent years, deep learning (DL) techniques, especially Convolutional Neural Networks (CNNs), have shown remarkable performance in image classification tasks. The proposed study presents a hybrid model for the multi-classification of fashion cloth images by combining the strengths of both CNNs and SVM. Using binary classification, the authors first divide the fashion clothing photographs into male and female categories. Then, multi-classify the images into four categories, including ethnic, casual, formal, and sportswear. The 5000 images that make up the dataset for the study have been divided into training and testing sets. The proposed hybrid model combines the feature extraction capabilities of CNNs and the decision-making power of SVMs to produce improved classification results. The results of the experiments show that the binary classification results in an accuracy of 95.5%, while the multi-classification results in the best accuracy of 96.24% in the case of the formal class of fashion cloth.
E-Commerce using Geo-Navigation

In the internet era, e-commerce is booming, exceeding the growth of brick and mortar businesses by a significant margin. Businesses are deciding to have an electronic commerce or Android-based counterpart. Individuals in the developed world and an increasing number of individuals in the developing world now frequently utilise ecommerce applications to make daily purchases. Even still, there is much to be desired, and e-commerce has not significantly taken off in the developing world. It involves the planning process, which starts with creating the database, domain modelling, and an architecture pattern for an Android application. This article describes the features of an e-commerce application with integrated GeoNavigation for the best way to address the difficulties associated with offline buying.
Machine Learning based Intelligent Cyberbullying Avoidance System

Cyberbullying has been an arising phenomenon that is mentally affects people especially youth and widely addressed among several research communities. It is deemed to be an essential problem to be addressed to avoid harassment on social media. Filtering a cyberbullying text before it gets posted can be effective rather than taking actions after posting. In this regard, this research study proposes an intelligent system to detect a cyberbullying post using machine learning algorithms. The proposed system is experimented with three algorithms namely Naive Bayes (NB), Support Vector Machine (SVM) and Neural Network (NN). The results achieved out of the study seems to be promising leading to higher accuracy in classifying a cyberbullying.
Ingenious: Text Summarization and Question Answering

Deep learning has attained many remarkable advancements over the past few years and is now rapidly developing in the NLP field. Abstractive automatic text summarization indicates the process of employing computer software that summarizes a document without altering the actual intent of the content. Several use cases for automatic summarization include creating headlines, summarizing scientific documents, segmenting search results, and summarizing product reviews. In the period of the Information explosion, large amounts of data, and the Internet, the ability to express the core meaning of information concisely will help address the information overload crisis. Traditional Techniques often relied on extractive summarization, which involves selecting and rearranging existing sentences or phrases from the source text to create a summary that may lack coherence and fail to generate novel sentences. Deciding what information to include or exclude and how to compress the content without losing important details is challenging. In this research, a BART-based model is used (a denoising autoencoder for pre-training inter-sequence models) to develop a data set trained for automatic analysis and summarization of long texts and articles. The model is pre-trained in English. Additionally, it uses a model based on the trained dataset to answer questions from the text. Our approach is based on textual answers provided by the community, making it easier to implement and answer more complex questions. The proposed schema explores various techniques such as question-and-answer classification and query generation. Finally, the test results are attached and evaluated.
Human-Computer Interface Challenges in Mobile Data Mining

The continuous advancement of computer software, hardware, and artificial intelligence, has allowed for tremendous progress in the development of human-machine communication systems in recent years. The development of such methods provides more accurate results than humans. The core of the human-computer interface is the Natural Language Understanding (NLU) component. The success of Human-Computer Interaction (HCI) and the time it takes to accomplish tasks are both directly affected by the results of this module’s semantic comprehension. In this research study, a neural network and HCI-based engine is developed for understanding and interacting with natural languages. Data from the appropriate literature is used to comprehend the necessary understanding of HCI. Next, the interactive engine is used to develop a model of HCI based on natural language comprehension using a neural network, and also the model’s underlying neural network reasoning structure is assessed.
Detection of Legitimate and Phishing Websites using Machine Learning

Many persons in the modern racing industry utilize moral means of subsisting, but some are choosing to go the route of cyberattacks, phishing being one of them. Delivering fraudulent messages that appear to be from a trustworthy source is a component of “phishing” attacks. For the attacker to get access to the system or account, a phishing campaign often seeks to obtain the victim’s information, such as their login and password. Using the phishing website prediction dataset, paper aims to create machine learning models by improving more features on various models to improve the better performance. The many phishing websites will be compiled in one place.
Role of Data Mining Across Different Formats of Multimedia

In today’s world, mining data from videos plays a significant role in the process of information retrieval. The generation of information pertaining to moving images is straightforward for many users. As a result of this, obtaining the required material, particularly the unstructured datasets is a challenging process. There are a number of factors it might contribute to this level of complication, including recordings captured by individuals without professional training, subpar cameras, inadequate lighting, and many more. There were several other algorithms it had been presented to recover useful video footage, but none of them achieved the results it was anticipated. It is, and will likely always be, the most difficult area for scholars to work in. A brand-new method for extracting video images is presented in this article, and it makes use of the image feature value as its primary tool. At first, the picture is grouped, and then the image input extract feature for particular information and then is used to get rid of undesirable image frames inside the grouped image. The results of the experiments demonstrate the procedure suitable for all kinds of moving pictures.
Handwritten Character Recognition System using Deep Learning Models for Tamil Language

Just like its countless achievement in resolving various computer image processing-based problems, the deep learning presented novel end-to-end tactic to many Handwritten Character Recognition (HCR) with very optimistic outcomes for many languages in recent years. Convolutional Neural Networks (CNN) are the most efficient technique adopted in computer vision and character identification with automatic feature extraction technique. This proposed research work recommends an innovative handwritten Tamil character identification system using AlexNet model. In Tamil linguistic, the multiple letters have alike features, therefore identifying exact letter is an exciting mission. This research work offered that, this deeper architecture can assistance for Tamil Handwritten Character Recognition (THCR) a lot to attain greater performance, in the meantime can be designed with less parameter. 12 primary, 18 consonants and other compound characters, totally 156 classes of Tamil character of Tamil language are recognized with better classification rate and attained nominal processing time using this model.
Smart Multi-linguistic Health Awareness System using RASA Model

The Artificial Intelligence (AI) chatbot is becoming more popular and has turned out to be one of the most prominent technologies for user assistance. AI chatbots are used to improve the customer experience by providing automated conversations, minimizing cost, and being capable of providing support in multiple languages. This research study has proposed a multi-linguistic health awareness chatbot for providing assistance to people. This AI chatbot will help people to get their queries answered related to diseases, treatment, and precautions. The advantage of this chatbot is that it is capable of providing information to users in their native language. This multi-linguistic chatbot is based on Natural Language Processing (NLP) model and built using the RASA SDK. To evaluate the proposed chatbot’s performance, the chatbot’s deployment is done using the RASA-X, which is the Conversation-Driven Development environment.
Development of Decision Support System on Online Payment Failures using Ensemble Learning

Machine learning algorithms are becoming more significant in one’s daily lives, influencing a wide range of societal and industrial aspects. Machine learning is changing the living and work, from personalized recommendations to autonomous vehicles. With the increasing reliance on online transactions, the detection and prevention of payment failures in real-time has become a critical aspect of business operations. This study proposes an efficient ensemble model that employs various machine learning algorithms for accurate detection of payment failures. Multiple algorithms are compared and integrated using ensemble learning techniques to create a robust decision support system. The study identifies challenges faced in payment failure detection and prevention and presents the proposed system as a solution. Proposed experimental results demonstrate the effectiveness of the proposed system in achieving high accuracy in detecting payment failures, making it a valuable tool for businesses. The training of an efficient ensemble model that detects and prevents these problems in the present research uses a variety of machine learning algorithms. Furthermore, the use of ensemble learning techniques in the process of building a decision support system will make it more robust. This research compared various algorithms to integrate the best one and create the proposed system. Therefore, the proposed system works well for the accurate detection of payment failures, which is important for any business development.
A Deep Learning Approach for Product Recommendation using ResNet-50 CNN Model

Product recommendation systems work well at boosting user engagement, increasing sales, and enhancing customer satisfaction by making personalized and relevant suggestions for goods based on users’ preferences and behaviors. However, these systems face several challenges, including data sparsity, scalability, and bias. Recent studies have looked into the creation of image-based recommendation systems using deep learning and transfer learning methods, such as Reverse Image Search, to overcome these difficulties. This study discusses the use of deep learning and transfer learning techniques, including Reverse Image Search, to create a product recommendation system based on images. The system draws features from images and produces five product recommendations based on similarities to other images using an already trained CNN model called ResNet-50. The study highlights the effectiveness of the approach while acknowledging potential limitations such as bias and scalability. To overcome these limitations, the study proposes further improvements, such as incorporating user preferences into the recommendation system and using more advanced deep learning models. The study concludes that the ResNet-50 model is a promising approach for image-based product recommendations, but further improvements are necessary to enhance its accuracy and scalability.
Rest API based Web Interface for Blogging Application

The purpose this research study is to deliver a backend service for frontend developers with help of REST APIs, fulfilling their requirements and full-fledged software so that they can utilized their crucialtime on frontend part. The Rest API for blogging application as described above can lead to error free, secure, stable, reliable and quick requests/responses. It can give assistance to the clients to concentrate on their frontend development rather to concentrate on backend code. Thus the client can utilize the resources in better way. User records of clients can be maintained without redundant API calls. Client will send data in JSON format through API call which will be verified on our server and will be saved to DB. The client refers to the API documentation to structure the request in a manner that the server can comprehend The server validates the client’s identity and ensures that the client is authorized. Server will receive a request from the client. The client will send a request to the server and it will operate internally. In return server will convey a response to client. The response will be containing the information that will inform the client whether the request was successful or not. In response the client will get all the information requested. This will result in efficient and reliable communication between client and server.
Context Swap: Multi-PIM System Preventing Remote Memory Access for Large Embedding Model Acceleration

Processing-in-Memory (PIM) has been an attractive solution to accelerate memory-intensive neural network layers. Especially, PIM is efficient for layers using embeddings, such as the embedding layer and graph convolution layer, because of their large capacity and low arithmetic intensity. The embedding tables of such layers are stored across multiple memory nodes and processed by local PIM modules with sparse access patterns. Towards computing data from other memory nodes on a local PIM module, a naive approach is to allow the local PIM to retrieve data from remote memory nodes. This approach might incur significant performance degradation due to the long latency overhead of remote accesses. To avoid remote access, PIM system can adopt a framework based on MapReduce programming model, which enables PIMs to compute the local data only and CPUs to compute intermediate results of PIMs. However, the multi-PIM system still suffers from performance degradation because the framework is processed on the CPU and it has a long delay compared to the PIM kernel execution. Therefore, we propose a context swap technique that prevents remote data access even without a high-latency framework. We observe that transferring PIM contexts to the remote PIM node needs much fewer data traffic than remote accesses of data. Our PIM system makes PIM nodes swap their context data with each other when they complete their own computation and no longer have local data to compute. Until all PIMs calculate all local data, several context swaps occur. The context swap is performed by a memory controller between PIMs in the same CPU socket and simple software between PIMs in different CPU sockets. To this end, the proposed multi-PIM system outperforms the base PIM system transferring remote data and the PIM system with the kernel-managing framework by 4.1 × and 3.3 ×, respectively.
A Hardware-Centric Approach to Increase and Prune Regular Activation Sparsity in CNNs

A key challenge in computing convolutional neural networks (CNNs) besides the vast number of computations are the associated numerous energy-intensive transactions from main to local memory. In this paper, we present our methodical approach to maximize and prune coarse-grained regular blockwise sparsity in activation feature maps during CNN inference on dedicated dataflow architectures. Regular sparsity that fits the target accelerator, e.g., a systolic array or vector processor, allows simplified and resource inexpensive pruning compared to irregular sparsity, saving memory transactions and computations. Our threshold-based technique allows maximizing the number of regular sparse blocks in each layer. The wide range of threshold combinations that result from the close correlation between the number of sparse blocks and network accuracy can be explored automatically by our exploration tool Spex. To harness found sparse blocks for memory transaction and MAC operation reduction, we also propose Sparse-Blox, a low-overhead hardware extension for common neural network hardware accelerators. Sparse-Blox adds up to 5× less area than state-of-the-art accelerator extensions that operate on irregular sparsity. Evaluation of our blockwise pruning method with Spex on ResNet-50 and Yolo-v5s shows a reduction of up to 18.9% and 12.6% memory transfers, and 802 M (19.0%) and 1.5 G (24.3%) MAC operations with a 1% or 1 mAP accuracy drop, respectively.
Blockchain based Skill Verification System

Ensuring the authenticity and transparency of data along with the prevention of its misuse has become an increasing concern in today’s data-driven economy. The use of blockchain helps in this regard to a certain extent, given its decentralized way of storing data combined with its power of making the data extremely hard to be mutated. This DApp (Decentralised application) being developed enables easy verification of skills and certifications of a particular candidate by storing the captured data on Ethereum Blockchain, thereby making the information stored immutable and secure. The proposed system has been developed in three phases. In the first phase, the institute or an organisation enrols the candidate or student, after which they are required to update their profiles and enter the necessary background information. In the second phase, candidates can raise a request for endorsements and verification of their skills and certifications. And in the third phase, organizations can accept or reject the endorsement requests. This skill-verification system prevents malpractices in the hiring process and ensures the validity of the data provided by an individual.
Image Frequency Separation Residual Network for End-to-end RAW to RGB Mapping

Due to the limitations of hardware specification of smartphones' camera system, there is still a visible gap in imaging quality between smartphones and digital singlelens reflex (DSLR) cameras. Sophisticated learning-based image processing becomes a promising solution to close this gap. In this paper, we propose an Image Frequency Separation Residual Network (IFS Net) to perform the end-to-end RAW to RGB image mapping. Different from existing methods that directly train the input image and the ground truth image one-to-one as a whole, our proposed method first divides the input image and the ground truth into high-frequency and low-frequency parts by discrete wavelet transform (DWT). These two parts are then trained separately using different networks for details and global information, and finally synthesized into the output image using inverse DWT. Experimental results show that the proposed IFS Net outperforms other existing algorithms in both PSNR and SSIM. Visual comparison shows that the images produces by IFS Net preserves more details and look close to that captured by DSLR cameras.
A Systolic Computing-in-Memory Array based Accelerator with Predictive Early Activation for Spatiotemporal Convolutions

Residual (2+1)-dimensional convolution neural network (R(2+1)D CNN) has achieved great success in video recognition due to the spatiotemporal convolution structure. However, R(2+1)D CNN incurs large energy and latency overhead because of intensive computation and frequent memory access. To solve the issues, we propose a digital SRAM-CIM based accelerator with two key features: (1) Systolic CIM array to efficiently match massive computations in regular architecture; (2) Digtal CIM circuit design with output sparsity predicition to avoid redundant computations. The proposed design is implemented in 28nm technology and achieves an energy efficiency of 21.87 TOPS/W at 200 MHz and 0.9 V supply voltage.