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The study analyzed scientific publications on students' problems in learning physics in different countries. Physics is considered to be a difficult subject that requires in-depth understanding of concepts and application in real-world contexts. In this context, bibliometric approaches are used to identify patterns in scientific literature and analyze related research. The aim is to provide useful information for educators, researchers, and stakeholders in developing more efficient and creative educational strategies. Through the collection, analysis, and interpretation of quantitative data, this method offers a better understanding of patterns, trends, and research contributions in this field. The results of the study showed a significant increase in research on student physics learning problems, especially in the United States and Indonesia. Expect international research collaboration and information sharing to understand students' problems in greater depth. These initiatives can help improve the quality of physical education and find the right solutions through the development of learning media that suits students' difficulties. With a better understanding of students' problems in physics learning, positive transformations are expected to occur in physical education globally [ABSTRACT FROM AUTHOR]

Power plants become critical facilities that need to run every time without failure. The component in the power plant system has been equipped with sensors. These sensors will monitor the element's condition by periodically sending the related metric data to the control room or storing it for later evaluation. These sensor data can be analyzed for early anomaly detection on power plants. Traditional methods still need humans as experts to detect these anomalies; hence, human error is a factor. Using deep learning, we can take advantage of abundant data to learn the usual pattern and detect anomalies in future data. Many deep learning algorithms have been implemented in this case. However, the selection of these algorithms depends on the data's characteristics. This paper aims to provide a comprehensive, structured analysis of the data features and the algorithm used to detect anomalies in the data. Twenty-two recent power plant anomaly detection studies were compiled as a systematic review. This review will discuss power plant sensor data characteristics that need to be considered, such as raw data format, label usage, data dependence, and detection results. This review also discusses methods used to process unsupervised data, the relation of the multivariate data, and anomaly detection. This review aims to ease the selection of the appropriate anomaly detection algorithm for future research by considering the data characteristics. [ABSTRACT FROM AUTHOR]

Rainbow antimagic coloring is a research topic on graphs that can be applied in everyday life. The graph neural network itself is a deep learning architecture that is used to solve machine learning problems on data in the form of graphs. Graph Neural Networks (GNNs) are a topic that researchers are currently discussing. GNNs are capable of performing tasks that Convolutional Neural Networks cannot do. By looking at existing literature, this research will examine the use of Rainbow Antimagic Coloring of Graph and Graph Neural Networks. The integration of Rainbow Antimagic Coloring of Graph and Graph Neural Networks is expected to be able to solve problems in life. Based on the results obtained from existing literature, the rainbow antimagic coloring concept can be combined with the neural network concept to solve real-life problems, including irrigation systems and supply chain management. [ABSTRACT FROM AUTHOR]

Dental disease is the first complaint of 10 major diseases in Indonesia. Almost the entire population of Indonesia has dental and oral health problems. Delay in early detection of dental and oral health problems can result in loss of productivity, longer duration of treatment, loss of teeth, higher costs, complications with other organs, which can lead to death due to cancer. We propose the invention concerns wireless oral image recording instruments and identification methods thereof. Several existing inventions do not yet have the following features including wireless connection, object detection, and prediction of oral and dental diseases that integrate the knowledge of clinical experts using the application. The purpose of this invention was to add to the features mentioned above by using Artificial Intelligence of Medical Things (AIoMT) concept, designing object detection based on deep learning (DL) algorithms, and integrating prediction systems high performance using collaborative datasets. This invention integrated intraoral image recording hardware in the Intraoral Camera Unit (ICU) with software in the Display Processing Unit (DPU) which produces visualization of predicted results, and a webserver unit as a data collection unit and image classifier according to the validated disease interpretation. The integration of these three units can then be used by medical scholars, researchers and medical practitioners to strengthen the reliability of the interpretation process, especially related to oral diseases using an integrated digital dental support system. [ABSTRACT FROM AUTHOR]

In this article, a method has been proposed to predict stroke on earlier basis to prevent any critical damage or death caused by it. The aim is to predict stroke in earlier basis so the necessary steps can be taken to stave off any critical damage. For this purpose, the parameters like heart disease, smoking habit, hypertension, glucose level, body mass index level, age, and work pressure have been considered. Moreover, previously happened stroke is also being considered to observe and empathize the situations and based on these facts real time stroke prediction analysis using deep learning and machine learning approaches has been suggested. At the present time "machine learning" and "deep learning" tactics are put on for finding the problems' solutions in "computer vision", forecasting and "convolutional neural network". This work proposes a before time prediction of stroke using various machine learning methodologies with occurrences of parameters based on which different classifiers are trained and data processing is done which attains a better outcome than other methodologies. [ABSTRACT FROM AUTHOR]

In Today's internet world, detecting Fake News is facing a difficult task. Data integrity has been of maximum importance in the current news era due to the extensive range of free text. Therefore, generating and developing fraudulent information is more painless than ever. The spreading of such fake news influences society from diverse inappropriate views. Online Social Networks has become increasingly popular to obtain news and information on the internet due to their widespread uses. However, false information has been spread widely due to the internet's rapid growth. As a result, detecting fabricated information is critical in our present culture. As a resolution, in this paper, a Deep learning approach is suggested to confirm the trustworthiness of news. It is also proposed as a Multimodal faked information detection method that utilizes both the textual and graphical content of the data to determine its authenticity. The first step is to pretrain the BERT model to achieve the textual feature representation vector. As a second step, pretraining the VGG-19 model generates a visual characteristic representation vector. Two MCBP (Multimodal Compact Bilinear Pooling) modules have been created based on the proposed methodology. Graphic feature representation vectors with alerts are obtained using the first MCBP module. MCBP's second module combines the visual feature with the attention process and the vector of textual elements. With a second MCBP module, the attention process and a textual feature vector are utilized to input visual quality. You may be able to exploit the linked vector to your advantage in detecting fake information. In this article, a comparison is performed between the proposed method and two standard methods. EANN and Spot Fake approaches have been compared in this work, and it has been found that the proposed strategy in this article outperforms both in terms of accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age and is characterised by disrupted ovulation cycles and an increase in androgen secretion. It is allied with metabolic and cardiovascular risks as well as a greater risk of developing hormone dependent-cancer. Due to its high prevalence in the current society coupled with its potential hazards, its early and accurate diagnosis is of utmost importance. The diversity of symptoms often delays the process of confirmatory diagnosis and more than 50% of women remain undiagnosed. To address these issues by facilitating rapid detection, this article proposes early diagnosis of PCOS with hormonal test and ultrasound images using various Machine Learning and Deep Learning algorithms. In this paper, we have used Generative Adversarial Networks to extend our dataset, and Transfer Learning using Residual Networks (ResNets) and Inception Networks for the ultrasound images. For the hormonal dataset, after pre-processing, we have compared the performance of multiple models, where CatBoost produces the highest accuracy. [ABSTRACT FROM AUTHOR]

Ensemble classifier of submarine acoustic wave enables efficient audio spectrum utilization using additional proposals includes noise reduction and fish and other aquatic organisms prevention. Acoustic transmission primarily employs various modulation schemes. Sensors and telecommunications can accomplish inside a comparable spectrum also with minimal possible of interference when such encoding pattern of a monitor and control is evaluated. Such mechanism too can reveal how well a system is being operated beyond the allowable parameters. In particular for undersea acoustic telecommunication, autonomous modulation prediction model sometimes use computer vision and optimization algorithms techniques. This research develops an entirely new array of learning-based modulation signal categorization algorithms for undersea acoustic transmission utilizing this as its driving force. In addition, several supervised neural frameworks are utilized to identify and extract first from before the transmitter square spectrum and periodic pattern. Furthermore moreover, black widow optimization has been employed to tweak the input variables of learning algorithm to optimum. The outcomes from the three deep learning models are therefore aggregated just use a combination of voting systems. In acoustic connection, the presented system can accurately perform forth a variation identification phase. The functionality of the proposed method gets evaluated below a range of instances, but a detailed evaluation of the results confirmed the supremacy to far more methodologies. [ABSTRACT FROM AUTHOR]

Harmful Algae Blooms (HABs) have emerged as a critical environmental concern, causing significant damage to marine ecosystems and coastal communities worldwide. Sabah, a coastal region in Malaysia, has experienced increased frequency and intensity of HAB events, leading to severe ecological and economic consequences. Traditional methods for detecting and predicting HABs are limited by their manual and time-consuming nature, hindering timely responses and effective mitigation strategies. To address this issue, this study presents an approach for predicting HABs in Sabah using a Deep Learning model. The proposed deep learning model harnesses the potential of artificial intelligence by integrating historical HAB occurrence records. In the first phase, the researcher collected and organized data from the Sabah Fisheries Department, then carefully cleaned and transformed it into a more practical format. After reviewing existing research, the study chose two promising techniques: the Artificial Neural Networks (ANNs) algorithm and the Long Short-Term Memory (LSTM). These techniques were experimented with to determine the most effective one for predicting HABs. Phase three involved rigorous testing of the prediction model using existing data, providing insights into how well it functions with the dataset and revealing the results obtained. In Phase four, the focus shifted to evaluating the model's performance, specifically identifying any reduced accuracy or shortcomings in the prediction percentages. This study adopts various evaluation metrics to assess the model's performance, such as accuracy, precision, recall, and F1-score, on a dataset with distinct training, validation, and testing sets. The authors also conducted usability testing to gather users' feedback on the proposed system. By providing timely predictions, the proposed model empowers stakeholders to take proactive measures to mitigate the ecological and socio-economic impacts of HABs on the marine ecosystem and coastal communities in Sabah. The findings will contribute substantially to the community by improving early warning systems and optimizing resource allocation for monitoring and controlling HABs. [ABSTRACT FROM AUTHOR]

At present, detection and recognition of animals remain a complicated task and no distinctive technique can solve all situations strongly and effectively. Animal detection in images is challenging because of the complexity of wild environments. Including several different things: The large differences in shapes and color appearances of variations of objects which belong to the same class, and Lighting/Illumination Conditions. This paper presents an effective and efficient animal detection system for Birds detection in images. MobileNet SSD model is a Single Shot Multibox Detection (SSD) suggested in this work, which is capable of handling different shapes and view angles of the objects. This approach allows extracting features of image regions and using deep learning techniques for classifying regions into two categories: (bird and non-animal). The experimental results demonstrated the efficiency of classification and detection of single and multi-animals even in difficult environmental conditions. The accuracy of detection and classification is up to 98.8%. This methodology is more accurate than other similar approaches. This paper exhibits a method for training a convolutional neural network (CNN) based on object detection classifiers. [ABSTRACT FROM AUTHOR]

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%. [ABSTRACT FROM AUTHOR]

The convoluted and dynamic nature of cancer growth makes it difficult to predict long-term cancer survival. Evolutionary deep learning techniques have been growing in popularity as efficient and accurate prediction techniques in recent years. Long-term cancer survival prediction has been developed to utilize evolutionary deep-learning techniques. This research combines the power of evolutionary algorithms using Particle Swarm Optimization (PSO) with some deep learning techniques to improve the performance and accuracy of the deep learning models by optimizing its parameters and minimizing the error between the predicted and actual outputs. Overall, combining evolutionary algorithms with deep learning models for cancer survival analysis prediction demonstrates the potential to significantly improve the accuracy and efficiency of cancer survival predictions, leading to better treatment decisions and improved patient outcomes. This paper uses the gbsg2 survival analysis dataset, a well-known dataset commonly used in survival analysis studies. [ABSTRACT FROM AUTHOR]

The global demand for energy is surpassing its availability owing to the increase in population and advancements in technology. It is essential to move closer to a dependable, economical, and sustainable renewable energy source to meet future energy demands. All alternative energy sources, including photovoltaic (PV) energy, have the potential to be renewable resources that can be used to address these ongoing issues with the energy crisis. PV systems are subject to the dynamic and fluctuating nature of the surrounding environment, which can lead to the manifestation of a diverse array of defects. It is essential to promptly identify and rectify any defects in order to ensure that the photovoltaic panel's efficiency, endurance, and durability remain uncompromised. The significance of automated defect detection is increasing in the PV industry due to the limitations of manual or visual inspection and the expanding production of PV modules. Therefore, this paper aims to develop a deep learning (DL) system that can accurately classify and detect defects in Electrouminescent (EL) images of PV cells, more specifically through implementing Convolutional Neural Networks CNN. Surprisingly, the suggested PV fault detection model outperformed commercially available PV fault detection systems by resolving existing challenges. The proposed model was further evaluated in terms of accuracy, loss, recall, f1 measure, and precision. The precision measure was 97.4% and the accuracy was 91.2%. [ABSTRACT FROM AUTHOR]

Facial palsy (FP) is an inflammation or disorder of the seventh nerve that leads to sagging of the facial muscles on the affected side and loss of control over movements and expressions. All ages and genders are susceptible to this disease. The medical diagnosis of this disease is prone to errors because it is based on the doctor's visual observation. Deep learning based technologies are the perfect solution for real-time FP detection. This study used a set of pre-trained deep learning based models including ResNet, AlexNet, DenseNet, GoogleNet and VGG16, to detect in real time FP, age and gender using a Raspberry Pi equipped with a digital camera. The accuracy obtained in the proposed system reaches 98% by using a wide variety of data consisting of 20,600 images, which includes 19,000 normal images and 1,600 images of persons with FP with different side of the injury. Thus, the proposed detection system can be considered as an effective and high accurate diagnostic system for FP, age and gender detection. [ABSTRACT FROM AUTHOR]

Water scarcity starts affecting the world and the trend remains the same even worse over the time due to the increasing global population which is expected to be more than 9 billion by 2050. The major portion of the fresh water was consumed by agriculture sector predominantly on irrigation. India has 4% of world's freshwater resources to assist 18% of world population. Unlike USA and China, India takes 2-4 % more water for its agricultural production. Continuous harvesting without the sufficient measurement and provisioning of the soil nutrient may endanger the sustainability of the agriculture. The amount of macro and micronutrients, pH level and water is used to measure the fertility of the soil. In case of soil nutrient deficiency, fertilizers to be added to the soil to maintain the nutrient levels. Most of the farmers apply the fertilizers manually. The right amount of nutrient to be maintained or else it may affect the plant life cycle and reduce the yield. Therefore, there is a need to adopt the advanced technologies in agro irrigation system by applying intelligent systems and services for the effective usage of fresh water and to maintain soil nutrient. Intelligent Irrigation System (IIS) which schedules the irrigation dynamically based on the soil features. Deep learning model for soil moisture and nutrient prediction, the same model can be deployed into other domains such textile and home automation. Mobile application to the farmers to know the status of the irrigation and soil fertility information. [ABSTRACT FROM AUTHOR]

Brain Cancer is the most dangerous type of cancer, which affects hundreds of thousands of people worldwide every year. Early identification of brain tumor is essential for timely treatment, which is crucial for the patient's survival. Researchers have found it challenging to develop Computer-Aided Diagnosis (CAD) for Brain Tumor Identification. Many studies have discussed the feasibility of Deep Convolutional Neural Network algorithms like ResNet, Inception, VGG in Brain Tumor Classification and Detection. These Deep Learning algorithms seem to have struggled from vanishing gradient, model bias with overfitting due to smaller medical datasets available. The proposed model TumorNet adopts a custom activated Deep CNN Architecture for classifying the more dangerous malignant tumors from benevolent tumors. The proposed model is trained from scratch and able to train with a smaller dataset consisting of MRI scans. It is evaluated, and it gives an impressive accuracy of 98.24%, outperforming other state-of-the-art CNN models, ResNet-101 and EfficientNet-B0. In comparison, the F1 scores of the proposed model are estimated at 98.05%, significantly higher than the other two models, which are 82.46 and 88.33%, respectively. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly infectious disease caused by the coronavirus and has resulted in a global pandemic since 2019. The virus evolves, mutating into several very subtle variants to escape the conventional infection testing methods such as RT-PCR, leaving them obsolete against such new variants. Due to a shortage of resources and rapidly growing infection and mortality rate, a much more robust and more efficient way of testing and diagnosing COVID-19 was developed using deep learning algorithm-based solutions that analyze Chest Radiographs. Existing algorithms suffer from model bias and lack knowledge interpretability by classifying the given image into two classes: healthy or infected. CovidXDetector is a deep learning-based algorithm that detects and localizes Chest Abnormalities on Chest X-Ray Radiographs with critical feature annotations in the image that serves as clinical evidence to diagnose the diagnosis COVID-19. Based on Cascade R-CNN, it achieves an Average Precision score of 0.642 and outperforms YOLOv5 and SSD based detection algorithms by over 14% and 22%, respectively. This study affirms that our proposed model is a practical, easy-to-use and efficient solution for real-time diagnosis of COVID-19 using Chest Radiographs. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to evaluate the efficacy of the Blaze Pose Algorithm, a new method for human pose detection, in comparison to that of Convolutional Neural Networks. Unlike the current method, which relies on static neural networks, Blazepose is a novel deep learning model for estimating human poses. Pretest power is 0.8, and there are 163 participants in each group. Data was gathered from the MPII Human Pose Dataset, which contains over 25,000 photos taken from various web videos. Researchers found that Blazepose's accuracy was slightly higher than that of a traditional neural network (90.3% vs. 40%). Independent sample T-test results show a 95% level of confidence that there is a statistically significant difference between the proposed and existing groups, with a significance value of P = 0.001 (P 0.05) 2-tailed. The Blazepose outperforms the traditional neural network in terms of improved accuracy, as seen by the comparison findings. [ABSTRACT FROM AUTHOR]

False news is characterized as a made-up story with a goal to bamboozle or to delude. In this paper we present the answer for the errand of fake news discovery by using Deep Learning structures. Fake news identification is carried out utilizing two AI calculations, Natural Language Processing Algorithm(N=10) and Deep Learning algorithm(N=10) calculations. False and True these two sorts of dataset is utilized for Fake news recognition, and it is gathered from kaggle.com. Dataset comprises lines and 6 fundamental boundaries that are connected with the False news that information gathered from twitter. For each gathering more than 30 examples are taken, and it is separated into preparing and testing. Accuracy for Natural Language handling calculation is 91.300% and for Support Vector Machine calculation is 77.500%. There exists an insightful critical distinction between Natural Language Processing Technique and Support Vector Machine calculations with p<0.05. Fake news location utilizing Natural Language Processing calculation seems to acquire higher precision than the Support Vector Machine calculation. [ABSTRACT FROM AUTHOR]

The aim of this study was to improve the accuracy in predicting COVID and tuberculosis using innovative deep learning algorithm FNN in comparison with SVM algorithm. The report was created using a typical local 3000 page format, and the data sets for COVID (N=154), tuberculosis (N=113), and normal (N=160) were obtained. The parameters were maintained as threshold by 0.5, enrollment ratio as 1, 95%,confidence level, G power at 80%. In accordance with regular protocol, the results were analysed using COVID and the tuberculosis login site. Higher mean values (83.9865) and error mean (0.3652) were obtained for standard algorithms compared to machine learning algorithms. There exists a statistically insignificant difference between the two groups (p=0.542, p>0.05). This study reduces the time required for manual classification to half and eliminates human error rate. [ABSTRACT FROM AUTHOR]

This study's goal is to estimate the price range for mobile devices using the Deep Neural Network algorithm and the Random Forest model. When N = 10 sample iterations were used to test the accuracy of the model for the validation of the mobile price range, two groups—the Deep Neural Network algorithm and the Random Forest model— with G power (value = 0.8) were studied. The Deep Neural Network model's accuracy results have a possible range of 84.183 percent, and the Random Forest model has an accuracy of (74.80 percent). The Deep Neural Network and Random Forest had statistically significant differences (p = 0.005). The Deep Neural Network technique outperforms the Random Forest approach in terms of performance and significance, according to the study findings for validating the pricing range for mobile devices. [ABSTRACT FROM AUTHOR]

Person identification based on human action recognition is a dynamic field that combines computer vision, deep learning, and motion analysis to identify and authenticate individuals based on their actions or movements. This technology finds applications in various domains, including security, surveillance, healthcare, and human-computer interaction. In this comprehensive overview, we will delve into the key components, methods, and models used in neural networks for person identification based on human action recognition. An analysis of algorithms working on the basis of these models for personal identification is presented. Also, currently, methods of evaluating and recognizing and identifying human behavior through video images are rapidly developing. Intelligent analysis and processing of video data for personal identification is one of the promising, fastest growing research areas and is widely used in various fields of human activity. In particular, it can be used to solve problems such as video surveillance for security and access to facilities for various purposes (security systems, control systems), fire protection systems, navigation, quality and quantity control of manufactured products, and many videosurveillance. This system helps to catch criminals at airports, railway stations and seaports. [ABSTRACT FROM AUTHOR]

As per our health is concern main parameter we can consider like sleep for our health regeneration, memorization and main aspect like recovery of our immune system. An essential part of overall health. Before detecting sleep issues in our body suppose if we have some machine or any ideas that helps to patients who is goes in narrow for their sleep issues for that if we can go with some tools and help us to analyzed before happening to sleep disorder. Using the algorithm of DL, we can explore the research in healthcare system. DL models helps to work with wide range of unformatted dataset which helps to understand how sleep issues are coming into human being based on some parameters we can learn some procedure. By using survey, we are going to specifies the different DL models are used to detect various types of problems present into the human being with different sleep stages. It compares different approaches by several authors who are working in this area, consider some different channels like eeg, ecg and many more. Also, they have worked with classification of DL models and different Methodologies with some pros, cons and conclusion. The main and very important aspect is sleep for regenerating some cells of body during sleeping time of any human being. As we consider better sleep in any people, they need to take proper nutrition, sleep, enough amount of H2O, breathing rate to balance. It is as important as eating, drinking and breathing, necessary for the normal maintenance of fitness and psychological filling well. There are many health issues are related to sleep disorders like sugar level, heart problems, long time sleeping, not getting sleep and many others. [ABSTRACT FROM AUTHOR]

Emotion recognition plays an important role in human-computer intercommunication, in the medical field, in call centers, etc. Gradually, the chances of Emotion Computing are increasing. The main six primary emotions are anger, disgust, fear, joy, sadness, and surprise. Many types of emotion recognition methods are available to recognize these emotions. But they mainly focus on facial expressions, speech, and gestures. Using visible emotion cues cannot capture people's true emotions. To get real emotions, emotion recognition using a Convolutional neural network (CNN), Recurrent neural network (RNN), and support vector machine (SVM), LSTM becomes essential. This article discusses various methods used for emotion recognition using RNN, LSTM using machine learning and deep learning techniques, data mining methods, and their databases. It also includes the experimental results and accuracy of each method. With this focus on building an emotionless machine, brilliance can be achieved. [ABSTRACT FROM AUTHOR]

Coronary atherosclerosis is a chronic & ever-evolving condition that can present clinically as anything from symptoms to acute coronary syndrome, heart failure, or sudden cardiac death. Coronary atherosclerosis develops and progresses due to environmental or genetic causes. Unquestionably, CVD is one of the primary causes of mortality worldwide. One of the most common disorders associated with CVD is CAD. Atherosclerosis, which restricts blood flow to the heart muscle, is the major cause of CAD. The amount of gold for geometrical assessment, angiography, is now used to examine atherosclerosis, which is a difficult condition to diagnose. The diagnosis of the lesion or visual evaluation by the clinician are absolutely necessary for the angiography. In this study, DL potent feature extraction abilities provide it a significant advantage in the area of defect diagnostics. A Resnet50, Resnet101, and VGG19 were used. A ResNet-50 pre-trained on a dataset of coronary artery segments was used to substitute the backbone. Three classification systems' effectiveness in predicting heart disease is examined and contrasted with earlier research. Numerical outcomes demonstrate the increased 95.2 % accuracy of percentage of risk estimation of suggested approach Resnet50 as contrasted to other two approaches. [ABSTRACT FROM AUTHOR]

The frequency and sophistication of cyberattacks have significantly increased over the past several years, it is crucial to mention. This highlights the need for effective and efficient methods for detecting and mitigating these attacks. Deep learning has been discovered to be an useful tactic for combating this issue, but there are still many open research questions and opportunities for improvement. As such, ongoing research and development in this field is crucial for staying ahead of the evolving threat landscape. Deep learning has evolved into a powerful technology for detecting cyber-attacks. We survey the most relevant deep learning models and datasets used for cyber-attack identification in this research. We will be discussing the various sorts of cyber based attacks that can be identified using deep learning, such as detection of network intruder, malware detection, as well as detection of anomaly in network in this survey. The most prominent deep learning architectures for cyber-attack detection, such as recurrent neural networks, deep belief neural network convolutional neural networks & autoencoders, are then examined. We also discuss the importance of using diverse and representative datasets for training and evaluating these models, and review some of the most widely used datasets in the field. At last, we talk about a few of the challenges and future headings within the field of profound learning for cyber-attack discovery. [ABSTRACT FROM AUTHOR]

The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]