Reviewerly/exHarmony huggingface dataset:
SentiBERT: A Novel Approach for Fake Review Detection Incorporating Sentiment Features with Contextual Features

Fake (deceptive) reviews have become a serious problem for online consumers, with the proliferation of online marketplaces leading to an increase in spurious reviews that are often used to lure or discourage potential customers. While sentiment analysis has been introduced to the e-commerce sector, the lack of an effective method to differentiate between authentic and fake reviews is still a major challenge. Existing approaches face issues such as slow convergence and inadequate precision. In order to address these challenges, this paper proposes a new approach that integrates sentiment features into the review detection process. The proposed approach uses a feature extraction method that utilizes a preconstructed sentiment dictionary, a pre-trained BERT model to extract feature vectors, and a fully connected dense layer to classify reviews as real or fake using the softMax function. The effectiveness of the proposed approach was evaluated on the Yelp dataset, showing a nearly 7% improvement in accuracy compared to existing feature sets and a nearly 4% improvement over existing state-of-the-art methods. The integration of sentiment features has shown promising results in detecting fake reviews, which is crucial for ensuring a fair and trustworthy online marketplace.
Quantum-resistant Blockchain Cryptography-based Smart City Transactions

Due to certain unique qualities and capabilities, Blockchain is a highly beneficial approach that may be used to safely manage various gadgets in a smart city. It has several uses, particularly in dispersed situations where elements such as wireless sensor nodes must be confident of the server's legitimacy. Because modern blockchain solutions that handle post-quantum problems have not been developed, we explore a blockchain in the quantum-resistant cryptography context and strive to find how it might withstand quantum computing assaults throughout this work. Furthermore, the newly born Proof of Stake (PoS) provides faster and cheaper transactions, but its security is not proven in comparison to its forefather Proof of Work (PoW). As a result, a new quantum-resistant proof of stake (Quantum-Resistant PoS) agreement technique for smart city application services has been developed. The proposed model has the ability to protect a distributed ledger system against a quantum assault and also provides scalability and inexpensive transactions. Additionally, user-based post-quantum authentication is included in the transaction process to create a simple payment verification node. Following that, we present a detailed rundown of how to execute a post-quantum simple payment verification and transaction on a blockchain. Thus, our study will contribute to imminent quantum-resistant blockchain exploration along with the design or structure of potentially distributed ledger technology-based ubiquitous computing.
Multi-Class Breast Cancer Classification from Digital Mammograms Using Vision Transformers

Breast cancer stands as a prevalent form of neoplastic disease afflicting women worldwide, with timely detection playing a pivotal role in ensuring favorable treatment outcomes. In this paper, we propose a methodology for multi-class breast cancer classification using Vision Transformers, a powerful architecture for learning long-range dependencies. CNNs utilizing deep backbones have demonstrated remarkable performance in medical image analysis tasks, including disease classification, tumor segmentation, and lesion detection. Nevertheless, CNNs' inductive bias towards locality limits their ability to extract global feature information from breast cancer mammogram images, resulting in suboptimal classification results. To overcome this shortcoming, Vision Transformer (ViT) is a promising deep learning model for computer vision tasks, but its applicability to medical image classification is limited by the need for large-scale datasets and high computational power. Our results show that Vision Transformers have the potential to outperform convolutional neural networks in image-based computer-aided diagnosis (CAD) schemes. DeiT is more efficient for small-scale datasets, as it can effectively learn general features through self-supervised pre-training. On the other hand, Swin Transformer's hierarchical structure and linear computational complexity make it a computationally effective alternative. We applied the proposed methodology on two variants of two different Vision Transformer architectures, Swin and DeiT, and achieved high accuracy and AUC values for differentiating normal, benign, and malignant cases. The models are trained on Inbreast dataset obtained from Kaggle after which they are evaluated on accuracy, precision, recall, the F1-score and training time. The DeiT-B transformer model beats the Swin-S on accuracy by 11.29% achieving a highest accuracy of 88.50% with ROC of 90, 92 and 96 for normal, benign and malignant classes respectively.
Anomaly Detection using Minimum Covariant Determinant as Feature in Multivariate Data

Ensuring reliability in the data generated by various applications enables data analyst to take accurate decisions But the presence of anomalous or faulty readings may affect the accuracy of decision making. Many statistical and machine learning techniques have been developed by research community and widely used for anomaly detection. To this end, this paper presents a method that leverages minimum covariance determinant based mahalanobis distance to improve the feature set and then refined feature set is supplied to various machine learning algorithm. The performance of the proposed approach has been evaluated on various machine learning algorithms including probabilistic model, statistics based binary classifier, Isolation based model and neural network-based model. It is observed from the results that the proposed approach reasonably improves the machine learning methods performance metrics such as detection accuracy, precision, recall and F1 score of the machine learning models.
Efficient Use of Blockchain for Crowdfunding Platform

Crowdfunding is a scheme of raising funds from many people or industries. Stakeholders can donate to any scheme they are concerned in and make money if initiative is positive or successful. Belief is a key component for everyone involved in generating funds, including the donor, the company that provides the fundraising platform's services, and even the fundraiser. However, traditional crowdfunding platforms have some limitations, such as high transaction fees, payment processing delays, and lack of transparency. So, this paper has emerged blockchain technology to address these limitations, offering a new model of crowdfunding that is decentralized, secure, and transparent. The proposed solution is a blockchain-based crowdfunding service platform in the form of a Decentralized Application (DApp), which further utilizes smart contracts and crowdfunding campaigns to execute the crowdfunding process. The proposed solution is time-saving, and cost-efficient and reduces the risk of the donations being used for malicious purposes.
Image Based Action Recognition and Captioning Using Deep Learning

Image recognition and captioning are two essential tasks in computer vision that have gained significant attention in recent years. With the massive increase in digital images on the internet, there is a growing demand for automated systems that can accurately recognize and describe visual content. Image recognition involves classifying an image into predefined categories, whereas image captioning generates a natural language description of the image. These tasks require advanced machine learning algorithms and techniques to analyze and understand visual content, enabling computers to recognize objects, people, and scenes, and generate descriptions that are accurate and relevant. This paper focuses on using machine learning and deep learning algorithms for image recognition and captioning tasks. Specifically, the paper introduces a machine learning approach to image recognition using the SURF algorithm for feature extraction and SVM for classification, comparing the effectiveness of different kernels. The paper then explores the capabilities of deep learning in image captioning, using a pre-trained RESNET50 and VGG16 model for feature extraction and an LSTM-based neural network for generating captions. The paper also compares the effectiveness of the deep learning models by evaluating their accuracy using the BLEU score. The paper comprises two main components: an ML-based image recognition module and a DL-based image captioning module. The ML-based module provides robust image recognition capabilities, while the DL-based module generates high-quality image captions using a neural network-based approach. The paper highlights the importance of these tasks in various industries and concludes that leveraging these techniques can lead to innovative solutions in diverse domains.
SVM Kernel and It’s Aggregation Using Stacking on Imbalanced Dataset

The imbalanced dataset’s existing classification methods have low prediction accuracy for the minority class because of the little information present. Using over- and under-sampling techniques, we can improve the minority’s ability to forecast outcomes. However, the minority class’s accuracy of prediction is negatively impacted by the two methods due to the loss of vital information or the addition of irrelevant details for classification. SVM kernels have great abilities to handle asymmetric data, but when we need to use SVM kernels alone or as part of the ensemble technique for an unbalanced dataset, we don’t have a strong reason to choose which kernel to use, and also how a particular kernel will act depends a lot on the data set. In this paper, we present a framework in which several kernel SVM (Linear, Polynomial, Sigmoid, RBF) classifiers were utilized as the base learners and one of the kernels (say RBF kernel) as meta learner using the Stacking Ensembles technique, which shows that stacked generalization of SVM kernels gives similar results as best performing kernel for an imbalanced dataset of software change proneness, using AUC, ROC, MCC, and BAS as an evaluation matrix.
Comparison of ML, Deep Learning and Bio-inspired Algorithms in Bug Triaging

Effective bug triaging is critical for efficient software development as bugs can cause software failures and unexpected behavior. The bug triaging process involves report summarization, severity classification, and assignment of bugs to developers. This paper introduced a novel developer recommendation algorithm designed to rank developers to reduce the bug tossing length. To automate and improve the accuracy of bug triaging, various machine learning models, deep learning models, and bio-inspired algorithms are utilized. In this project, we compared the effectiveness of bio-inspired algorithms with traditional machine learning algorithms such as SVM, Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Random Forest. We utilized open-source datasets such as the eclipse bug dataset and Firefox dataset for our analysis. We preprocessed the data and used the Artificial Bee Colony (ABC) algorithm for optimizing bug triaging. Our results indicate that the ABC algorithm yields more accurate results with fitness score 78.603 as compared to traditional machine learning algorithms in which highest accuracy obtained was 0.56 by decision tree.
Fog Computing and Edge Computing: Open Issues, Critical Challenges and the Road Ahead for Future

Internet of Things (IoT), one of the trending and emerging fields in the world today, has sufficed the purpose of interconnection and interlinkage of devices and smart applications with extremely little hassle. Its success has led to an exponential rise in the integration of numerous technically and mechanistically sound methods and ideas with IoT in order to climb the ladder of evolving perspectives and successful developments. However, his too had a number of challenging phases which were to be hurdled over in a smart and fiscal manner. The need to accommodate the massive amount of distinct data being generated called for the efficacy of Cloud Computing. Cloud Computing was initialized in the 1960’s and gained popularity over the last decade. Unfortunately, Cloud Computing didn't prove to be the all-in-all solution to the consequences and weaknesses of IoT. Later, Cloud Computing evolved and improvised to a novel version of Fog/Edge Computing which is highly in demand among the new-fangled. Safety and security of the data reservoired is a conundrum for majority of the researchers and workers in this field. The tactical incorporation of Blockchain technology into that of Fog/Edge Computing would be one of the best solutions to prevent data breaches and other forms of intrusion attacks.
Video Annotation &amp; Descriptions using Machine Learning &amp; Deep learning: Critical Survey of methods

Video description methods aim to produce the most relevant description of a video. This could be description based on full video, frame based or based on important events of the video. This work focuses on recent advancement of video description methodologies in the form of a survey. The work spans across most of the latest deep learning methods along with other classical algorithm-based methods. The categorization of the methods found is done on various aspects like weakly supervised, Tree/graph based, Attention based, Multi Modal based, Supervised with Attention, Attention on region importance based, Video to Q/A based etc. We have performed a comprehensive comparative analysis of the methods surveyed. The relevant application areas as well their relevancy is also mapped to the categories discussed in this paper. A method's relevancy to the application areas is also complemented with the metrics to be used for evaluation. Lastly, the paper also discusses the future directions pertinent to the business requirements presented in recent Forbes's articles and our insights on the survey.
Intelligent Hardware for Preventing Road Accidents Through the Use of Image Processing

Road transportation is the most common and oldest mode of transportation. It provides quick connectivity to small businesses having customers nearby areas, transportation of perceivable products in nearby markets. Not only the nearby market, also for international market supply transferring goods from the supply unit to the railway station, ports, or airports. Also, from day to day life commute from home to the office for a service class person, from home to schools/colleges for students, commuting to hospitals, banks, malls are done by road transportation only. It has an essential contribution to economic growth. Although road transportation is an essential and most common mode of connectivity, it has a higher probability of road crashes than any other transportation mode. These results a lot of injuries, deaths in every year and every city. Government has lot of road safety protocols, rules, and regulations to prevent road accidents. Every year government is spending a lot for road safety. Not only government car/vehicle manufacture keep on redesigning the vehicle with multiple inbuilt safety equipment’s. This paper is proposing a device with a key feature of lane detection. The device is featured with monitoring of engine, tires and other peripherals. The device can be mounted in vehicle, and can make an ordinary vehicle to semi-smart vehicle. This paper explains a feature of the device that is lane detection. An identification of tracks and obstruction on streets is not an easy task to detect with proximity sensors and an IR sensor. So that a digital image processing based lane detection technique had been implemented in this paper. This technique is very efficient to detect the road path and obstructions on the street on different scope of ecological conditions under which these frameworks work: rain, shadow, daylight, day, night, haze and so forth.
Insider Threat Detection: Using Classification Models

Insider threats refer to cyber-attacks originating from within an organization that can cause significant damage, such as intellectual property theft, sabotage, and sensitive data exposure. Traditional cybersecurity strategies tend to focus on external threats, leaving organizations vulnerable to insider attacks. In this paper, we propose an approach for insider threat classification with various classification models. Aggregated numerical features are generated using the access patterns of the employees of the organization. We used the CERT dataset for training and testing. The proposed method is evaluated with classification models like Logistic Regression, Decision Tree, Random Forest, and Xgboost. The experimental results of the model's performance, measured using evaluation metrics such as accuracy, recall, precision, and F1-Score, demonstrated improved accuracy and performance compared to existing works in terms of high recall, precision, and F1-Score values, and effectively outperformed pre-trained CNN models.
Cybersecurity Named Entity Recognition Based on Word-level Enhancement and Multi-task Learning

At present, the situation of cybersecurity is becoming increasingly serious, and the study of Named Entity Recognition (NER) in the field of cybersecurity is helpful to automatically extract cybersecurity entities. It is of great significance for the subsequent analysis of cybersecurity. However, less study is done in the area of cybersecurity and the majority of the current research only applies to the general field. And for the dataset mixed with Chinese and English in the field of cybersecurity, the existing NER method is difficult to solve the problem of ambiguity in word boundaries. To solve this problem, this paper proposes a cybersecurity NER model based on word-level enhancement and multi-task learning on BERT-BiLSTM-Att-CRF (MTLWE). One the one hand, MTLWE obtains character-level feature vectors through the BERT pre-trained language model. In order to strengthen the word boundary feature information, the cybersecurity word embedding vector is obtained by Word2Vec algorithm. And the combination of these two vectors can enhance feature information. On the other hand, in the model, we configure the model to train the NER task and the Word Segmentation (WS) task alternately. In this way, the WS information is fused into the NER task and it produces the impact of multi-task learning. At the same time, adversarial training is used to filter the private independent information of WS task to ensure the purity of the common features of multi-task learning. Finally, the MTLWE model is experimented on the constructed cybersecurity dataset, and the F1 value reaches 65.22%. The results show that the MTLWE model shows better performance than other baseline models.
Sampling-Based Optimal Motion Planning Algorithm with disjointed-Trees in Constrained Environment

Sampling-based motion planning techniques have produced many efficient solutions in robotics research. Motion planning has expanded from straightforward use cases to challenging application settings, but it is challenging to perform in a constrained environment with obstacles. In this study, we suggest a rapid exploration random tree-based motion planning method that starts at various motion planning stages, from sampling to connection. By processing the sampling points close to the obstacles on the one hand, the suggested technique optimizes the spread of points in the workspace. On the other hand, it creates numerous free arms to increase workspace visibility. Together., the two elements increase the tree's development and the connectivity of its paths in challenging workspace locations. Numerous irregular obstacles and tight spaces are created for experiments, and the suggested motion planner is contrasted with several traditional motion planners. According to experimental data, the proposed method is more stable in providing optimal solutions and requires less sampling time in the limited passage scenario.
Global Multi-Object Transformer Tracking with Detecting and Attention Mechanism

Visual multiple-object tracking (MOT) has always been a research hotspot in the fields of computer vision and video image intelligence analysis. Tracking algorithms must not only accurately locate targets but also adapt to various types of interference while meeting robustness requirements. However, the uncertainty of the number of objects in the scene, mutual occlusion between objects, and the low discriminability of object features have led to slow progress in the practical application of visual multiple-object tracking. To address this issue, we designed a modified attention mechanism module and embedded it into a transformer-based global multiple-object tracking architecture, generating global trajectories for all objects. This forms the proposed efficient algorithm for object tracking. For larger convolution kernels, our method effectively extracts each feature in the receptive field while reducing computation costs, thus improving algorithm efficiency. We conducted the experiments on MOT17 and TAO, and achieved the state-of-the-art results. Experimental results show that our proposed method outperforms existing models overall, ensuring stable object trajectories while maintaining high-quality detection.
Human Target Detection and Tracking Algorithm Based on Improved YOLOv7

Recently, human-oriented target tracking has broad application prospects in various practical fields, such as medicine and security. YOLOv7 is a powerful detection algorithm in the current main-stream, and it still has room for development in terms of detection accuracy. To handle the issue of mobile robots struggling to maintain recognition precision and stability in complicated scenes, we propose an improved target detection network for YOLOv7. Firstly, based on YOLOv7-tiny network structure, the CBAM attention mechanism is introduced into the bottom of the backbone network, and the SPPCSP module is adopted to reduce the computation of the feature map, which could enhance the network's representational capability. Then, the KCF algorithm is incorporated into the model, which could track the human target after determining its position with respect to the robot with the RGB-D camera. In order to ensure the flexibility of the robot, the Jetson Nano is utilized as the processor. Finally, we conduct human target tracking experiments for the mobile robot in a realistic scenario. The experimental results show that the mAP reaches 91.17%, which is 1.88% higher than only using YOLOv7 after introducing the CBAM module.
Optimizing Badminton Action Recognition with Deep Learning and Sensor Fusion: A Study of Sensor Numbers and Combinations

It has become increasingly important to devise corresponding training plans by analyzing athletes' movements during matches and training. In order to achieve badminton action recognition using multiple sensors, we have constructed a badminton action data acquisition platform based on two inertial sensors, one electrocardiogram sensor, and one electroencephalogram sensor. This paper mainly utilizes two models, AlexNet and Googlenet, to recognize different badminton actions. We conducted several experiments to investigate the impact of different sensor numbers and combinations on recognition results.