Reviewerly/exHarmony huggingface dataset:
A Platform for Anonymous Tip-Off and Evidence Validation

In this modern world, the crime rate is rising rapidly, however, people are reluctant to share tip-offs (Evidence) with the investigating authorities, this is mainly because their personal information and identity may get compromised, posing a threat to their own personal safety. The paper proposes to build a centralized web-based platform for anonymous tip-off reporting and validation that aims to address the challenges faced by authorities in handling such information and provide an effective solution using technologies like Web Development, Machine Learning, and Cryptography. Tip-offs can be provided in the format of images, videos, texts, and audio files. The submitted tip-offs will be validated using Machine learning algorithms like SVM (Support Vector Machine), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and Natural Language Processing methodologies for validation and relevance to the information given by the user. Further, the whole data pipeline will be secured by AES (Advanced Encryption Standard) cryptographic algorithm. If the information is deemed to be credible and actionable, it is forwarded to the relevant authorities which will further take action towards it. The suggested approach has shown significant improvement in performance compared to existing methods and makes it accessible to the user to submit tip-offs at any given point in time
Design of Intelligent Medical Integrity Authentication and Secure Information for Public Cloud in Hospital Administration

Due to political and financial considerations, large hospitals are also less likely to share their patient information with outside healthcare providers. To get around the barriers that prevent an efficient process of exchanging medical data. The integrated computerized clinical information system is part of the Hospital Information System (HIS), which aims to improve hospital operations and clinical management. Furthermore, the patient has access to an accurate electronic medical record that has been stored. For research and statistical applications, such records can be utilized in a data warehouse. The architecture of a centralized information system, on which HIS was established intended for the rapid transmission of both operational and administrative information. It would be difficult and It requires a lot of money and resources to set up an independent information management system for a small village hospital. The hospital information system in use presently, information is only shared within the same hospital. The theory of cloud computing serves as the proposal's basis. The “cloud” makes it possible for greater analysis, sharing, and exchange of medical data from images. Doctors may be able to get the data they need due to cloud-based medical image storage, patient will be able to get treatment across hospital departments automating the management of hospital information and computational resources. Hence, this system develops of intelligent medical integrity authentication and it is more effective for hospital administration to use secure information on public clouds, low-cost and time saving.
An Advanced Auditory Response for Object Detection Using Deep Learning

An aspect of computer vision called object recognition searches for significant items in images and movies (by creating a bounding box). According to WHO, there are atleast 2.2 billion visually impaired persons in the globe. Their mobility is limited in strange environments due to their vision impairment. This research study intends to convert the image to text and then text to voice. Here, an intelligent system is proposed to recognize the common objects and generates verbal feedback to inform the user about the object's position. The best YOLO (You Only Look Once) version is selected after being compared. Following the process of object detection, the proposed model determines its spatial location before generating the speech output.
Speed Detection using Haar-Cascade Classifier and Pixel per Meter

Modern highways cannot exist unless traffic safety is prioritized. As a result, while determining speed restrictions for a certain route, the condition of the road and its tendency for accidents are considered. Speed monitoring cameras are strategically positioned along the route to detect motorists who exceed the speed limit. The traditional speed radar gun has been replaced with speed monitoring cameras. The main purpose of this research work is to create a speed-detecting camera by using image processing to process the videos using Haar Cascade in Python.
Federated Learning for Image Captioning: A Comprehensive Review of Privacy-Preserving Collaborative Model Training in Distributed Environments

This study presents a comprehensive review of the use of federated learning in the context of image captioning in distributed environments. It focuses on key aspects such as privacy preservation, data locality, and collaborative model training. The evolution of federated learning and its unique characteristics are explored, along with an examination of available open-source frameworks specific to image captioning. The study categorizes different approaches to federated learning for image captioning and showcases recent applications in diverse domains, including medical imaging, edge computing, autonomous vehicles, social media, and cross-domain image analysis. Additionally, optimization techniques, security analysis, and research challenges are discussed, encompassing data heterogeneity, privacy preservation, communication efficiency, limited labeling, scalability, and robustness against adversarial attacks. This comprehensive review contributes to a deeper understanding of federated learning for image captioning and highlights areas for further research and advancement in the field.
Design and Performance of A Parabolic Concentrator Solar System embedded with IoT Monitoring System

This paper presents a dual-axis tracking system of the parabolic concentrator solar system and remote solar monitoring system. The proposed approach is to motivate emerging technologies to reduce the dependency on fossil fuels and optimize existing systems to minimize energy consumption. Among the referenced renewable sources of energy, the sun, or sun-powered energy, is the most plentiful and sustainable source that substantially gives humans more energy than they need. The solar system’s effectiveness is a significant fuel source in the transition to clean energy creation, reducing this problem. Hence a parabolic concentrator is introduced, an electric generator with high-intensity irradiance using a dual-axis tracking system and embedded IoT monitoring system. Thus, collected validated data have recorded the voltage generated by the parabolic solar panel concentrator shape as the parameter used. The results prove that it produces 35% to 40% more than the flat surface. The results revealed that the recorded voltage ranges between 12V to 13V, and the temperature when the sunlight radiance vertically aligned on the solar panel is around 32–34C°; meanwhile, the surrounding humidity is around 65–79%.
Cloud Security Analysis using Blockchain Technology

Cloud computing is now widely employed across a variety of IT-related fields. It has become an essential technology to address infrastructure and data service requirements with minimal effort, low-cost, and with a high degree of scalability. Although the use of cloud computing has increased significantly, information security concerns have not yet been fully resolved. There are still certain information security concerns that must be resolved in order for cloud computing to advance. In terms of integrity, authenticity, and secrecy, in particular, Blockchain has developed into an essential security instrument. The application of Blockchain in cloud computing security is further examined in this article after a review of the different security concerns in blockchain and cloud computing. The main challenge in the cloud computing is cloud computing security. Because data in the cloud must be transported via the Internet, data security becomes a top priority. It is necessary to maintain the primary data protection methods, such as integrity, accountability, privacies, access controls, authentications, and authorizations. Blockchain is a technological advancement that enhances cloud computing. Blockchain resolves cloud computing security problems. This study seeks to analyze and contrast various cloud environment security challenges with blockchain security difficulties.
A Comprehensive Analysis on Unconstraint Video Analysis Using Deep Learning Approaches

Unconstraint video analytics are important in visual learning. Unconstrained videos contain complex content with various artifacts, variable lengths, and different operating environments. Human activity plays an important role in the video, abundant in archives and becoming more prevalent on the Internet. Various methods are employed for action recognition under constrained conditions, but huge attention to complex actions and realistic applications is substantially needed. Complex movements consist of sequences of simple movements that have long temporal structures. Complex actions in the same class exhibit large variations in class-interior appearance and behavior due to complex temporal structures, confusing backgrounds, changes in viewpoint, and changes in movement speed. Thus, feature representation and classification of complex motions are determined to be challenging in unconstrained video analysisdue to complex temporal structures. This article presents a comprehensive analysis to solve this problem using deep learningapproaches.
FCN Based Deep Learning Architecture for Medical Image Segmentation

By eliminating the laborious and time-consuming task of manually drawing contours around organs and tumors, deep learning algorithms have revolutionized the area of medical image segmentation. Deep learning can significantly improve treatment outcomes for patients with gastrointestinal cancer and many other medical illnesses where precise and effective segmentation is essential for successful treatment by automating the segmentation process. Fully convolutional neural networks (FCNs), a type of convolutional neural network, have been researched for medical image segmentation. In this study, a U-Net model, a form of Fully Convolutional Network (FCN), is evaluated for its use in segmenting brain tumors. The precision and accuracy of medical image processing have been proven to be greatly improved by deep learning-based techniques. Deep learning model interpretability, generalizability across different imaging modalities, and the requirement for larger and more varied datasets are still problems that need to be overcome. This research work intends to provide a performance and generalizability analysis of the U-Net model for medical image segmentation in brain tumor detection.
Comparison of Fuzzy-C-Means Method with K-Means Algorithm and Self Organizing Maps Using Image Quantization

There are several applications for the crucial colour quantization process in the disciplines of graphics and image processing. The majority of quantization methods are built on data clustering algorithms. Hard c-means (k-means) clustering methods have recently been found to be effective in this field. Other investigations came to similar conclusions with the fuzzy c-means approach and Self-Organizing Maps (SOM). The three different clustering techniques weren't explicitly compared in any of these experiments, which is interesting. In this study, we develop quick and accurate variations of the hard and fuzzy c-means algorithms coupled with SOM and a variety of starting strategies, and then compare the resulting quantizers on a wide range of images. The findings show that images produced by K-means and SOM had a little lower PSNR value than those produced by the fuzzy c-means algorithm. Due to their widespread use in this sector, along with Hierarchical clustering and Density Based-Spatial Clustering of Applications with Noise (DBSCAN), we have picked FCM, K-means, and SOM for comparison. Although we haven't discussed about hierarchical clustering and DBSCAN because they are space and time costly algorithms.
Advancements in Image Compression Techniques: A Comprehensive Review

Image compression is crucial for shrinking the size of digital photos without reducing quality. The demand for storage and bandwidth capacity continues to exceed available capacity, making image compression a necessary technique. The article compares and contrasts a number of lossy and lossless image compression methods, including Wavelet coding, transform coding, Run length coding, Huffman encoding, and Arithmetic coding. The study makes broad recommendations for identifying the best compression strategy for an image. The primary benefits, drawbacks, and research opportunities of these techniques are discussed to aid in selecting the appropriate compression method. The article concludes by stating that image compression is an essential technique that enables faster file transfer, storage optimization, and efficient use of physical storage resources.
An Investigation of Deep Neural Network based Techniques for Object Detection and Recognition Task in Computer Vision

Detection of objects and its recognition in visual sequences are the two critical tasks in the computer vision field. Various real-time applications such as autonomous vehicles, face recognition, health-care systems and space exploration requires highly reliable and precise object detection models. Traditional object detection and recognition algorithms are based on hand crafted and are considered to be erroneous, time consuming and expensive leading to the significant reduction of accuracy rate for object detection in large datasets. Recently, large number of promising deep neural networks models have been emerged for facilitating automated and accurate detection of varying scale objects and its precise recognition across various computer vision applications. Several GPU based neural models thereby incorporating context-aware capabilities have shown effective performance, which overcomes the drawbacks of traditional techniques. This research study provides an investigation of several popular deep learning models that exists for accurate object detection in various forms of visual sequences. Varying scale objects are detected frequently available in popular MS COCO and PASCAL datasets and their performance are evaluated utilizing one stage Yolo family and two stage Faster RCNN deep learning object detectors. At the end of the study, several future research directions for object detection task are discussed.
Privacy Preserving Secure and Efficient Detection of Phishing Websites Using Machine Learning Approach

One of the major worldwide crimes, phishing entail the burglary of the user's secretive information. Phishing websites frequently target the websites of business, institutions, government, and cloud storage space providers. While using the internet, the best parts of individuals are not aware of phishing assaults. Several phishing techniques now in use don't inefficiently address the troubles caused by email attacks. To combat software attacks, hardware-based phishing techniques are now deployed. The proposed effort concentrated on a three-stage spoofing series attempt for precisely identifying the difficulties in a material manner because of the increase in these types of problems. Uniform resource locators, circulation, and internet content based on phishing attack and non-phishing website strategy aspects were the three input variables. A dataset from previous phishing campaigns is gathered to apply the suggested phishing attack technique. Realistic phishing cases were found to provide a higher level of accuracy in phishing detection mechanisms and zero- day phishing attack. The categorization accuracy for phishing recognition using three dissimilar classifiers was indomitable to be 95.18 percent, 85.45 percent, and 78.89 % for NN, SVM, and RF, correspondingly. The findings indicate that a method based on machine learning works the best for phishing detection.
Efficient Video Object Segmentation based on Local-Global Consistency Learning

Efficient video segmentation algorithms are urgently needed as the rapid development of the multimedia technology has made real-time video data the mainstream of information transmission and analysis. This research work proposes a novel video object segmentation based on local-global consistency learning. In the designed model, local-global consistency learning algorithm is proposed. The core idea is to pass on the label information of each sample point to its neighboring samples through an iterative algorithm until all of the samples have reached a steady state of the label. Then, by applying this into the video frame analysis, the efficient segmentation is achieved. To solve the problem of information integration, the mixed features of the coded output are mapped to the frequency domain for the decomposition. By using the average precision and recall, the test is conducted. The performance is validated and the visualized results on the different collected images are tested.
LightRing-MO: Light Sensor Static-Authentication Using a Semi-Biometric Interacting with a LightRing

At this time, the smartphone is one of the most popular and commonly utilized instruments for protecting private information. One of the smartphone authentication methods is biometrics. Yet, the age difference (i.e., the elderly versus children) impacts the validation technique. Derealization and amnesia are the most critical issues many confront when validating sensor-based behavioral and physical biometrics. This study suggests a novel authentication method for smartphones called LightRing-MO that uses a light source with a valid value (a ring controlling a light source) and is based on static authentication. It works by processing the luminometer sensor found in mobile smartphones. A smartphone luminometer may be used to show how the authentication process works while disclosing specific information for each light ring. Numerous users may be able to utilize the light intensity, and the light value may provide a wide range of authentication options. It is difficult to reproduce due of each user’s unique lightness value. The three participants—two adults and one child—exhibited a high accuracy rate of 98.33% throughout the trials thanks to their previous training. The next stage is to enlist additional individuals to investigate and thoroughly examine the implications of this approach.
Fund Crypt: Blockchain based Crowdfunding Platform using SHA-256 &amp; POS Algorithm

This study explores the potential of utilizing blockchain technology in crowdfunding. Crowdfunding has gained significant popularity as a means of project funding, enabling individuals and businesses to raise capital through online platforms from a diverse pool of investors and donors. However, traditional crowdfunding platforms face various challenges, including high fees, lack of transparency, and vulnerability to fraud. This research paper aims to investigate how blockchain technology can address these challenges and enhance the crowdfunding experience by leveraging decentralized networks and smart contracts. Blockchain-based crowdfunding platforms offer a decentralized and transparent ledger, providing unique features that can revolutionize the crowdfunding process. The findings of this survey are valuable for entrepreneurs, investors, and developers seeking to create and utilize such platforms in a repeatable manner.
Time-Series Data Prediction Using Johnson-Lindenstrauss Lemma, Fuzzy Logic, And Self Organizing Maps

This paper uses a hybrid model combining Self-Organizing Maps (SOM), Johnson-Lindenstrauss Lemma(JLL), and Fuzzy Logic for time-series data prediction. It is named as SJLF model. SOM is used to group data having similar characteristics. By JLL, high-dimensional data is projected to low-dimensional space by approximately preserving the distance between the input vectors. The fuzziness in data is also carried on to the projected values. These projected values are input into the fuzzy logic system to obtain the predicted value as output. For experimental analysis, the available data on COVID-19 is taken from humanitarian data exchange. The proposed SJLF model is applied to five coronavirus-affected countries Belgium, Brazil, Colombia, India, and Iran. The SJLF model's prediction shows promising results as the average MAPE for five countries is 1.199, and the prediction accuracy on an average is 98.8%. The proposed model is compared with the ANFIS model and is found that the proposed model shows better forecasting results.
Offline Based Signature Verification Using Deep Neural Networks

Every individual has different and unique signatures which are used for authentication and identification of important document. Mostly used to certify the originality of cheques, draughts, certificates, approvals, letters, and other legal documents. A signature is employed in such crucial processes that it is important to confirm its legitimacy. In a number of financial, legal, and professional situations, this kind of verification is essential for preventing document fraud and falsification. In the past, signatures were manually checked against replicas of real signatures. This approach might not be adequate as technology develops and new methods for signature forgery and falsification emerge. Hence, a new effective tool is required to address this issue by adopting a convolution neural network technique. The proposed system assists in determining whether the user's new signature matches the original signature in the dataset. For authentication of offline signatures our research presents an application, which uses deep neural network models like Inception v3, Xception and ResNet50. ResNet50 has outperformed the other two models.
RFID based Smart Cart using Load Cell

Every industrial sector is evolving with the recent technological advancements. Though physical shopping in places like grocery stores, supermarkets remain constant for many years with no advancement. In huge cities because of their tight schedule during weekdays, People tend to shop during weekends. This causes a huge queue in the billing counter. Normally people approach the counter for billing purposes. Using a QR code, the cashier generates the bill but because of the rush, it becomes a time-consuming process. This research study overcomes this issue by using an RFID reader, LCD screen and a weighing scale.
Preserving Patient Privacy in IoT Based Breast Cancer Monitoring System

Disease detection plays a crucial role in providing right treatment to the patients. Currently, the lack of information on cancer prevention and awareness results in high fatality. Breast cancer is the second most deadliest disease across the globe. This type of cancer affects women than men. The proposed model employs an Internet of Things (IoT) based hand held device to predict breast cancer and transmit the heathcare data over the cloud for further medical analysis. The confidentiality of the patient's information is likely to be compromised during transmission. The patient's personal information might be intercepted by an outside party. Hence, the proposed model encrypts sensitive information using a mix of the Triple DES and AES method before sending it to the cloud. To improve the classification performance of MLP and SVM, Particle Swarm Optimization (PSO) is used, and a grid-based search was also used to determine the optimal settings of CNN and ANN models. To put the strategy to test, researchers used the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The proposed model has achieved 98.5% accuracy in CNN classification and 99.2 % accuracy in ANN classification.