Reviewerly/exHarmony huggingface dataset:
Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco

Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewable energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.
Impact Machine Learning Classification And Technical Indicators, Forecast The Direction Of Bitcoin

This study assesses the effectiveness of machine learning (ML) models in predicting fluctuations in the direction of Bitcoin prices using conventional technical indicators. Given the volatile nature of Bitcoin markets, achieving precise predictions is of utmost importance. The study improves machine learning models including logistic regression (LR), decision trees (DT), random forests (RF), support vector machines (SVM), Xgboost, and artificial neural networks (ANN) by integrating established technical indicators. By employing Optuna for hyperparameter optimization and the 'Time Series Split' technique for cross-validation, the study optimizes the models for time series data. The results demonstrate that ML models, when integrated with technical indicators, significantly outperform predictions based solely on those indicators. Evaluation metrics include accuracy, recall, and the F1 score. Through the incorporation of signal indicators, ML models offer a robust approach to predicting changes in Bitcoin prices. The proposed ML approach achieves a test accuracy exceeding 65.15%, highlighting the potential synergy between traditional financial prediction tools and contemporary technology. This study underscores the potential effectiveness of combining ML models with technical indicators for forecasting Bitcoin price movements.
Reliable secure Medical data sharing model based Private Blockchain structure

The current e-health systems suffer from multiple difficulties due to their centralized system structure for managing Electronic Medical Records (EMR), this approach exposed security and privacy issues, leading to serious degradation of healthcare services and potentially compromising the confidentiality and integrity of private patient data. In this paper, we proposed a consistent architecture that adopts a decentralized design of an e-health monitoring system, incorporates Edge Computing, Internet of Medical Things, Hyperledger fabric network, and IPFS aims to guarantee a secure data exchange environment, while addressing the major existing e-health system's challenges in terms of performance and security aspects. Based on the existing centralized structure to manage medical data, the current structures reveal critical security vulnerabilities affecting personal data. By taking advantage of a distributed structure the proposed architecture provides a high-scale secure environment that facilitates exchanging medical data over a protected network, aligning with the STRIDE Threat model structure. In addition to embedded technologies including strong encryption algorithms and Role-based access control (RBAC) deployed to offer substantial security standards, and effectively mitigate various advanced cyber-attacks. This decentralized e-health design demonstrates an impactful improvement model evidenced by the experimental performance results and revealed a better security experience for sharing medical data among multiple stakeholders.
From Development to Deployment: An Approach to MLOps Monitoring for Machine Learning Model Operationalization

Machine Learning Operations (MLOps) has emerged as an innovative and critical discipline in the development and deployment of Machine Learning models, ensuring smooth and efficient model management throughout their lifecycle. In this paper, we propose an implementation of MLOps concept using a pipeline. For instance, we propose a solution to automate the deployment of Machine Learning Models in production, while ensuring the monitoring of various predefined metrics, in order to guarantee the prediction quality of a model throughout its deployment. Maintaining the ML model in production, while exploring the possibility of using the AutoML concept, is essential to guarantee simplified, automated model management, enabling effective adaptation to change in data and continuous improvement in system performance.
Reinforcement Learning to Calculate Routes for Simulated Robot Safety Cones

This paper presents a model of a cone using reinforcement learning, harnessing the self-learning capacity of Artificial Intelligence to improve process efficiency. The independent operation of the cone is achieved through a reward and punishment system based on approaching or reaching the goal. The cone must decide between 0° or 90° turns at each step to maximize long-term rewards. While the simulated robotic safety cones successfully reach their targets, the training process is time-consuming due to the numerous variables involved. Nonetheless, the rise of AI and its self-learning capabilities offer promising opportunities for process optimization.
View Invariant Human Action Recognition using Surface Maps via convolutional networks

In action recognition, varied views are very challenging task due to same action appeared in different from different views. To address these problems, we proposed a novel ActionNet framework for perspective view invariant human action recognition system based on convolutional neural networks (CNNs) trained with multi-view dataset captured by 5 depth cameras. Recently, maps are cauterized geometric feature like joint locations, distance, angle, velocity, or combination of features used for skeleton based action recognition. Against their success, features represent earlier find complex in representing a relative variation in 3D actions. Hence, we introduced a novel spatiotemporal color-coded image maps called a joint relational surface maps (JRSMs). JRSMs are calculated subset of three joints in a sequential order covering all joints. In literature, single view depth data with multi steam CNN to recognize human actions but cannot recognize accurately view invariant actions. In this work, we trained by multi view action data with single stream deep CNN model for recognizing view invariant actions. To test the performance of proposed architecture, we compare our results with other state-of-the-art action recognition architecture by using our own multi-view 3D skeleton action dataset named as KLU3DAction and two benchmark skeleton action datasets like NTU RGB-D and PKU-MMD.
An Early Cancer Prediction Based On Deep Neural Learning

Cancer is the one of the most leading cause of deaths among people The lifetime chance of acquiring this malignancy is one in eight for females. The prognosis for this condition greatly improves with early detection. Understanding the role of these genes in the disease is crucial. However, the complexity of gene data comes from there being too many aspects to study. Therefore, we need medical support from intelligent algorithms. Others have utilized prognostic approaches like machine-learning(ML) &deep-learning(DL) to foretell prognoses for tumor, and many researchers now use them to estimate the likelihood of patient survival. Predictive cancer prognosis accuracy is a hot topic right now. For accurate disease forecasting, deep neural learning (DNL) approaches are indispensable due to their speed and accuracy in making predictions based on massive amounts of clinical and genetic data. In this research, we present a DNLC for early cancer prediction based on deep neural learning. Experiments are run on the five cancer datasets to foretell the model's efficacy. It is capable of analyzing mammograms and picking out abnormalities that may be a sign of malignancy. To further enhance their predictive abilities, these frameworks can incorporate additional patient information, such as age and family records. Incorporating attention mechanisms into breast cancer prediction tasks is the focus of this research. Our proposed approach makes use of multimodal data to produce informative traits that enhance prognosis prediction for breast cancer. The first part of the model employs a convolutional neural network with sigmoid gated attention to generate stacked features, and the second part employs flatten, dense, and dropout operations to facilitate bi-modal attention. The experimental findings demonstrate that the proposed technique outperforms the conventional CNN and RNN models in terms of accuracy. Our results show that the DNLC method is the most effective overall, with an average accuracy of 95%.
Real Time Object Detection in Video Surveillance Using Fast-D Algorithm

With the proliferation of video surveillance devices, the value of computer-assisted detection of anomalous occurrences in video streams has increased. Abnormal prevalence can also be viewed as an abnormal dip compared to the normal course. However, because the relationship between normal and abnormal is particularly unbalanced with respect to reality, many unnatural occurrences have become less frequent. Techniques have been proposed to detect unnatural video events based on both Convolutional Neural Networks (CNNs) and instance-based communication. This strategy has been used previously to recognize the need to localize anomalous video events within pixel-level regions. First, we use a Gaussian background model to accurately identify moving objects in the movie, and then use image processing techniques to capture the relevant regions of the identified moving objects. Finally, according to the purpose of use, the prepared according to the suction function from the combined area will be distributed among the systems, and then used according to the development of some core field packages. Finally, the multi-instance learning model learns how to use the normalized Embark-Head quotes approach to make pixel-level predictions. However, the (Fast-D) target detection method is applied depending on the detection of two-lane accidents. Based on our experimental results, video exception detection methods based on CNN or sparse illustration commands can accurately detect strange occurrences in pixel-composed environments. With this in mind, the reason I created this essay challenge was to find the first solution to the same problem using sound teaching techniques. This was done to avoid the need to include ethnic sources within the scope of anomalous activity to the extent that one would expect live speech to be observed outside of a system of rules.
Streak – Social Media Platform with Dynamic Identification of Spam Words, Phishing Urls, Misleading Products and Agencies

Social networking websites are growing prevalent online. Online users are more likely to spend time on social networking sites and other websites. Social media users make new friends while also exchanging information, ideas, and opinions. Customers may find a variety of helpful information on social networking sites. The amount of data on social media invites spammers who misuse the information. These spammers create fake accounts and send irrelevant information to actual customers. Spam messages may contain information like advertising for deceptive goods or dangerous phishing links meant to upset normal users. Spam on social media platforms is a major problem. Spam detection is a significant part of social media networking systems. Different spam detection algorithms have been developed by researchers to extract spam content from social media. We have utilized the machine learning model SVM (Support Vector Machine) and employed big data for data analytics as a classification strategy to identify spam in the social network. Blockchain is used to make it challenging to change saved data and to let users access it from several servers. SHA-256, digital signatures, and Merkle tree encryption are just a few of the techniques used to encrypt the data file.
A Study on Malicious Node Detection in Different Application Domains

Nowadays security and privacy in online networks is always questionable due to various factors in terms of cyber security in the form of attackers present outside the network. In this paper we have taken malicious node detection as a major point of research and done a survey on existing technologies in malicious node detection. This paper we have analyzed applications were malicious node has to be detected. Malicious node detection solves the issue of security and privacy in networks by using various approaches such as federated learning, machine learning, differential privacy, convolution neural networks, and deep learning concepts. This paper is organized in to various focuses on introduction, literature survey, methodologies and its comparison, conclusion and future ideas.
A Detailed Study on Security and Privacy Analysis and Mechanisms in Cloud Computing

When it comes to delivering IT services to businesses or consumers, the cloud provides a scalable, reliable, and cost-effective option. However, there is an increased risk associated with cloud computing due to the fact that critical operations frequently go to another organization, making it more difficult to preserve privacy and security of data, sustain data and availability, and prove compliance. It is a difficult issue in cloud computing to ensure the security of outsourced data. Data integrity should be verified by the client or an external auditor before being outsourced. Many methods using various techniques, including Proof of Retrievability, proved data ownership, privacy preservation, etc., have been developed for this goal. Various security strategies to address the current security vulnerabilities have been offered in the literature by researchers and impacted companies. Security and privacy concerns in cloud computing are also well reviewed in the literature. Unfortunately, the literature presented works lack the adaptability to counter diverse attacks without undermining cloud security goals. As a corollary, the literature has highlighted security and privacy vulnerabilities without giving suitable technological techniques to alleviate both security and privacy risks. However, studies that provide technological responses to security issues have not provided enough reasoning for the existence of these dangers. The purpose of this article is to discuss potential cloud security and privacy challenges that might arise and need a flexible approach to finding a workable solution. This study provides a comprehensive review of the relevant literature, discussing how safe cloud conflicts have made the suggested models from prior works obsolete while also taking into consideration the works' adaptability to handle future threats. The STRIDE methodology is then used to convey, from the user's point of view, the security concerns associated with cloud computing. It also analyzes the literature's many ineffective solutions and gives guidance on how to set up a safe, flexible cloud infrastructure.
Secured Electronic voting system using Blockchain Technology

Electronic voting has become an increasingly popular topic of discussion in recent years but it has some security issues. Ethereum's smart contract capabilities make it possible to develop tamper-proof and transparent e-voting systems. Smart contracts are self-executing programs that are stored on the Ethereum blockchain, and can be used to automate the process of voting and ensure that the results are accurate and verifiable. This paper explores the use of Ethereum for e-voting, including the advantages and challenges of using this platform for voting. The paper discusses the use of smart contracts for implementing e-voting systems, the security and privacy considerations that must be taken into account, and the potential benefits of using Ethereum for e-voting. The paper also discusses the challenges that must be overcome to ensure the widespread adoption of Ethereum-based e-voting systems, including issues related to scalability, interoperability, and regulatory compliance. In conclusion, this paper argues that Ethereum has the potential to revolutionize the field of e-voting, offering a secure, transparent, and efficient solution for conducting political elections. While there are still many challenges to overcome, the use of Ethereum for e-voting represents an exciting opportunity for governments, businesses, and individuals around the world to participate in democratic processes in a more secure and efficient way.
Ai Hologram Video Generator

This visionary concept seeks to revolutionize the field of holographic video generation by leveragingthe immense potential of deep learning methodologies. The overarching goal is to create an innovative system that can seamlessly convert standard 2D videos into mesmerizing 3D hologram videos, which can be experienced through a simple and accessible pyramid structure. This novel approach aims to eliminate the complexities associated with conventional methods that require intricate camera setups and multifaceted perspective captures.The journey begins with the acquisition of a diverse dataset containing 2D videos, forming the bedrock for subsequent transformations. In the initial stages of data preprocessing, pivotal features are extracted from the input videos. Following this, a dynamic process of spatial and temporal transformations unfolds. This intricate choreography enlists the services of a Convolutional Neural Network (CNN), which takes on the role of extracting spatial features from each individual frame. The CNN’s discerning gaze captures intricate visual nuances, bringing them to life within the holographic realm. Concurrently, a Long Short-Term Memory (LSTM) network enters the stage, diligently capturing the subtle dance of temporal dependencies. This synergy augments the resulting holograms with an enhanced sense of depth perception, evoking a truly immersive experience.At the heart of this innovation lies a Fusion Neural Network, meticulously designed to harmonize the spatial and temporal features into a cohesive symphony. This orchestrated fusion births a feature representation that epitomizes the marriage of visual dimensions. This harmonious blend embarks ona transformative journey through the realm of a Generative Adversarial Network (GAN). Within the GAN’s adversarial embrace, the feature representation unleashes a process of alchemical creation, conjuring forth holographic scenes that are breathtakingly lifelike and endlessly dynamic. This adversarial interplay, where a generator contends with a discriminator in a bid to refine the authenticity of the holographic fabric, is the crucible in which realism is forged. The grand culmination of this voyage is the emergence of a sophisticated system, poised to unravel the very fabric of conventional 2D videos. This astonishing transformation is executed with an exquisitely orchestrated pipeline of neural networks, wherein spatial, temporal, and generative forces synergize. The result is an enchanting array of holographic content, beckoning viewers to traverse the realms of imagination. The pyramid structure stands as both portal and pedestal, inviting audiences into an awe-inspiring experience that transcends the boundaries of traditional video playback. This audacious idea envisions a future where digital and physical realities intertwine, granting us a glimpse of what lies beyond the ordinary horizon of visual storytelling
Deepfake Detection: A Multi-Algorithmic and Multi-Modal Approach for Robust Detection and Analysis

In a time when deepfakes are eroding the reliability of digital media, our innovative research introduces a multi-faceted framework that achieves unprecedented levels of detection accuracy. Boasting a 97% success rate in verifying visual content and an almost unblemished 98.5% in audio analysis, our system serves as a formidable barrier against the malicious alteration of digital assets. Central to our model's stellar performance is the seamless integration of convolutional neural networks (CNNs) with ReLU activation mechanisms, all fine-tuned via stochastic gradient descent (SGD). This expertly engineered architecture is highly proficient at analyzing the nuanced spatial features of visual media, and it works in synergy with cutting-edge machine learning algorithms. For the audio detection aspect, we employ random forest algorithms, celebrated for their robustness and versatility. This ensemble learning approach adds an extra layer of complexity to the model, effectively identifying the intricate spectral and temporal characteristics of audio streams, thereby boosting the overall efficacy of our detection system. Our methodology is further fortified by meticulous data preprocessing methods, such as normalization and data augmentation, which ensure the model's robustness against a myriad of deepfake techniques. This groundbreaking research not only establishes a new benchmark in the arena of deepfake detection but also has significant ramifications for the wider field of cybersecurity and the preservation of digital authenticity. With its unmatched performance metrics, our research represents a pivotal advancement in combating the growing menace of deepfakes in today's digital society.
An AI Based Chat Bot For Banking Applications using Intelligent Chat Bots

A chat and voice assistant that is powered by AI and is integrated into a banking application. This assistant translates data in such a way that the text and audio are designed to simulate human conversations. Since their inception, voice and chat assistants have seen substantial technological advancement. The advancements that have been made in machine learning and natural language processing have made it possible for them to now learn from their experiences and conduct conversations that are more human-like. Because of their more human nature, they are able to assist with a wide variety of tasks, such as calculating loan interests, confirming facts regarding transactions, and checking savings.
Personal Voice Assistant Using Computer Vision

Modern society has made daily life smarter and more technologically connected to make it easier to go about our daily tasks. A voice assistant converts speech to text ,then process the text, convert text to speech and add various functionalities. In the era of smart personal assistants, Amazon Alexa, Apple Siri, and few other voice assistants have found popularity. A voice assistant interacts with humans by listening to their verbal commands and acting upon them. "SIRI," the most well-known iPhone app, allows voice communication between users’ mobile devices and responds to their voice commands. In addition, Google developed a similar technology called "Google Voice Search," which is used on Android smartphones. However, these applications mostly utilizes internet connections. Our proposed model works without internet connections and can manage tasks efficiently.
Classifying Diverse Material Images based on Transfer Learnin

Transfer learning plays a significant role in classification tasks within the domain of expert systems. It entails applying knowledge acquired from one job or topic to another that is unrelated but nevertheless uses that knowledge. Utilizing pre-trained models and features, transfer learning has been particularly effective in enhancing the performance of classification models. This paper evaluates the impact of pre-training upon a sizeable dataset, investigates adjusting on a more compact target dataset, and assesses accuracy and efficiency improvements. It also explores transfer learning’s potential to address challenges like limited labelled datasets and improve the generalization capabilities of deep neural networks in material classification tasks.