Hole Detection Research Articles

A Community Data Set for Comparing Automated Coronal Hole Detection Schemes

Automated detection schemes are nowadays the standard approach for locating coronal holes in extreme-UV images from the Solar Dynamics Observatory (SDO). However, factors such as the noisy nature of solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these automated schemes. While discrepancies between detection schemes have been noted in the literature, a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal Hole Boundary Working Team in the COSPAR ISWAT initiative to close this gap is threefold. First, we present the first community data set for comparing automated coronal hole detection schemes. This data set consists of 29 SDO images, all of which were selected by experienced observers to challenge automated schemes. Second, we use this community data set as input to 14 widely applied automated schemes to study coronal holes and collect their detection results. Third, we study three SDO images from the data set that exemplify the most important lessons learned from this effort. Our findings show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar and heliospheric physics. We envision that this community data set will serve the scientific community as a benchmark data set for future developments in the field.

Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography

BackgroundAutomated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans.MethodsIn this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman’s correlation was run to examine if the algorithm’s probability score was associated with the severity stages of IFTMH.ResultsSix hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman’s correlation coefficient of 0.15 was achieved between the algorithm’s probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied.ConclusionsThe DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm’s probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs.

5G Network Coverage Hole Prediction and Detection Using Machine Learning

A signal-free area in a wireless network is called a coverage hole (CH). The signal at this location is either nonexistent or too weak to be detected or monitored. There may sometimes be coverage gaps or places with poor radio frequency (RF) performance due to wireless infrastructure components’ inability to adapt to changing RF dynamics and offer adequate coverage of the locations. Finding coverage gaps and RF problem spots needs a client-side approach rather than the traditional infrastructure-driven solution because of the importance of network intuition. This article’s goal is to locate coverage gaps or weak signal places in a variety of scenarios, including 5G KPIs and QoS parameters (QCI, or quality of service class identifier). The primary objective is to apply classification techniques to determine which use cases or network slices are impacted by the decreased signal strength. Training and test datasets for supervised machine learning techniques are pre-collected measured report data from a live 5G network monitoring counter and data system. Since most KPIs are numerical data, the study uses the classification methods ANN, RF, NB, and LR. This is not at all like the traditional methods—such as driving tests, etc.—for gathering data for coverage-hole detection. Orange Canvas and Microsoft Excel are two instances of data mining technologies that are used for both detection and prediction.

A small and vigorous black hole in the early Universe

Several theories have been proposed to describe the formation of black hole seeds in the early Universe and to explain the emergence of very massive black holes observed in the first thousand million years after the Big Bang1–3. Models consider different seeding and accretion scenarios4–7, which require the detection and characterization of black holes in the first few hundred million years after the Big Bang to be validated. Here we present an extensive analysis of the JWST-NIRSpec spectrum of GN-z11, an exceptionally luminous galaxy at z = 10.6, revealing the detection of the [Neiv]λ2423 and CII*λ1335 transitions (typical of active galactic nuclei), as well as semi-forbidden nebular lines tracing gas densities higher than 109 cm−3, typical of the broad line region of active galactic nuclei. These spectral features indicate that GN-z11 hosts an accreting black hole. The spectrum also reveals a deep and blueshifted CIVλ1549 absorption trough, tracing an outflow with velocity 800−1,000 km s−1, probably driven by the active galactic nucleus. Assuming local virial relations, we derive a black hole mass of log(MBH/M⊙)=6.2±0.3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log ({M}_{{\\rm{BH}}}/{M}_{\\odot })=6.2\\pm 0.3$$\\end{document}, accreting at about five times the Eddington rate. These properties are consistent with both heavy seeds scenarios and scenarios considering intermediate and light seeds experiencing episodic super-Eddington phases. Our finding explains the high luminosity of GN-z11 and can also provide an explanation for its exceptionally high nitrogen abundance.

An Improved Coverage Hole Finding System for Critical Applications Based on Computational Geometric Techniques

Wireless Sensor Networks (WSNs) contain coverage holes caused by both random node sensor deployment and malfunctioning nodes. Because fixing the battery is challenging, collaborative discovery and assessment of coverage shortfalls, as well as getting rid of these holes, has been recognized as critical in WSNs. While placing nodes for sensors in a large-scale WSN is challenging. This research provides a cost-effective coverage hole detection approach based on collaborative distributed point placement. Create a polygon first by employing an angle estimate approach and neighbor data. Following that, a based on points hole identification technique is used to assess if a coverage issue appears in a large-scale WSN's supplied ROI. Furthermore, the region of the coverage hole is estimated using computational geometry-based polygonal triangulation methods. The accuracy of the method is tested here using statistical data. The results show that it outperforms earlier coverage hole-detecting algorithms. In particular, the method improves coverage rate by 75% when compared to conventional methodologies. It also lowers energy usage by 90%, adding to increased network lifetime. The quantitative favourable results demonstrate the effectiveness of the collaborative distributed point placement technique in detecting and successfully resolving coverage gaps in WSNs. In regards to coverage rate, energy consumption, and network longevity, the system being proposed beats previous coverage hole-detecting techniques.

Biomarkers in macular hole surgery

A macular hole is a condition that affects the central part of the retina, the macula. It is characterized by the formation of a hole or tear in the tissue, which can lead to blurred or distorted vision. Early detection and treatment of macular holes are critical to prevent permanent vision loss. Biomarkers are measurable indicators of biological processes that can aid in the diagnosis and management of macular holes.One of the most commonly used biomarkers for macular holes is optical coherence tomography (OCT). OCT uses light waves to create high‐resolution images of the retina, allowing for the detection and monitoring of macular holes. OCT can also be used to measure the size and depth of the hole, which can help guide treatment decisions.Another biomarker for macular holes is the presence of cytokines and growth factors in the vitreous, the gel‐like substance that fills the eye. Cytokines and growth factors are signalling molecules that play a role in the inflammatory response and tissue repair. In patients with macular holes, the levels of certain cytokines and growth factors are altered, indicating the presence of inflammation and tissue damage.Recent studies have also identified genetic biomarkers associated with the development and progression of macular holes. For example, mutations in the FZD4 gene have been linked to an increased risk of macular holes, while variations in the CX3CR1 gene have been associated with a more severe form of the condition.Biomarkers can also be used to monitor the response to treatment in patients with macular holes. For example, changes in cytokine and growth factor levels following treatment can indicate whether the treatment is effective in reducing inflammation and promoting tissue repair. Similarly, changes in OCT measurements can indicate whether the hole is closing and vision is improving.Overall, biomarkers play an important role in the diagnosis, management, and monitoring of macular holes. By providing objective measures of disease progression and response to treatment, biomarkers can improve outcomes for patients with this condition. However, more research is needed to fully understand the role of biomarkers in macular holes and to identify new biomarkers that may aid in the diagnosis and treatment of this condition.

Dancing with the Stars: a Review on Stellar Multiplicity

Stars like company. They are mostly formed in clusters and their lives are often altered by the presence of one or more companions. Interaction processes between components may lead to complex outcomes like Algols, blue stragglers, chemically altered stars, type Ia supernovae, as well as progenitors of gravitational wave sources, to cite a few. Observational astronomy has entered the era of big data, and thanks to large surveys like spatial missions Kepler, TESS, Gaia, and ground-based spectroscopic surveys like RAVE, Gaia-ESO, APOGEE, LAMOST, GALAH (to name a few) the field is going through a true revolution, as illustrated by the recent detection of stellar black holes and neutron stars as companions of massive but also low-mass stars. In this review, I will present why it is important to care about stellar multiples, what are the main large surveys in which many binaries are harvested, and finally present some features related to the largest catalogue of astrometric, spectroscopic and eclipsing binaries provided by the Non-Single Star catalogue of Gaia, which is, to date, the largest homogeneous catalogue of stellar binaries.

Energy‐Efficient Routing Algorithm for Optimizing Network Performance in Underwater Data Transmission Using Gray Wolf Optimization Algorithm

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.

Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques

Net inspection in fish-farm cages is a daily task for divers. This task represents a high cost for fish farms and is a high-risk activity for human operators. The total inspection surface can be more than 1500 m2, which means that this activity is time-consuming. Taking into account the severe restrictions for human operators in such hostile underwater conditions, this activity represents a significant area for improvement. A platform for net inspection is proposed in this work. This platform includes a surface vehicle, a ground control station, and an underwater vehicle (BlueROV2 heavy) which incorporates artificial intelligence, trajectory control procedures, and the necessary communications. In this platform, computer vision was integrated, involving a convolutional neural network trained to predict the distance between the net and the robot. Additionally, an object detection algorithm was developed to recognize holes in the net. Furthermore, a simulation environment was established to evaluate the inspection trajectory algorithms. Tests were also conducted to evaluate how underwater wireless communications perform in this underwater scenario. Experimental results about the hole detection, net distance estimation, and the inspection trajectories demonstrated robustness, usability, and viability of the proposed methodology. The experimental validation took place in the CIRTESU tank, which has dimensions of 12 × 8 × 5 m, at Universitat Jaume I.

CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains

This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs face, particularly the decreased detection accuracy in complex grassland environments due to shadows and obstructions. The model incorporates a Context Augmentation Module (CAM) focused on improving the detection of small mouse holes and mitigating the interference of shadows. Additionally, to enhance the model’s ability to recognize mouse holes of varied morphologies, we have integrated an omni-dimensional dynamic convolution (ODConv), thereby increasing the model’s adaptability to diverse image features. Furthermore, the model includes a Task-Specific Context Decoupling (TSCODE) module, independently refining the contextual semantics and spatial details for classification and regression tasks and significantly improving the detection accuracy. The empirical results show that when the intersection over union (IoU) threshold is set at 0.5, the model’s mean average precision (mAP_0.5) for detection accuracy reaches 92.8%. The mean average precision (mAP_0.5:0.95), calculated over different IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05, is 46.2%. These represent improvements of 3.3% and 4.3%, respectively, compared to the original model. Thus, this model contributes significantly to grassland ecological conservation and provides an effective tool for grassland management and mouse pest control in pastoral areas.

Application of YOLOv8 and Detectron2 for Bullet Hole Detection and Score Calculation from Shooting Cards

Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduces an automatic score detection model using object detection techniques. The study contributes to the field of computer vision by comparing the performance of seven models (belonging to two different architectural setups) and by making the dataset publicly available. Another value-added aspect is the inclusion of three variants of the object detection model, YOLOv8, recently released in 2023 (at the time of writing). Five of the used models are single-shot detectors, while two belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640 × 640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among these models, YOLOv8m performed the best, with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3 ms) than YOLOv8m (5.7 ms) and yet generated a competitive mAP50 of 96.5%.

Study of a Small Robot for Mine Hole Detection

China is rich in coal resources, but complex hydrogeological conditions lead to difficulties in coal mining, including coal mine collapses, roof and water damage, and other accidents that occur frequently, resulting in many casualties and property losses. The use of coal mine hole detection technology to detect and analyze the internal environment of the coal mines in advance helps to reduce safety hazards and prevent coal mine accidents; however, the operation of existing coal mine hole detection technology is cumbersome, difficult to control, and encounters problems due to an insufficient depth of jacking. This paper designs a new type of small robot for mine hole detection. Firstly, we analyzed the function and structural design of the mine hole detection robot, designed a variable diameter function according to the characteristics of narrow and uneven mine holes in coal mines, and analyzed the mechanics of the critical parts using theoretical calculations. Secondly, using three-dimensional modeling software (Solidworks 2019), we established a structural model of the small robot for mine hole detection. After that, we designed a hardware circuit and control program for the robot and emphasized the safety design of the circuit, considering the presence of water and gas inside the coal mine. Finally, to verify the feasibility of the design program, the basic parameters and function tests of the mine hole-detection small robot were carried out. The experimental results show that the developed mine hole-detection small robot can adapt to working hole diameters from 65 mm to 100 mm and has a maximum working power of only 12 W and a maximum crawling speed of 3.96 m/min. The maximum crawling slope reaches 90°, which can meet existing mine hole inspection needs. This research provides theoretical and design guidance for developing mine hole-detection robots with substantial engineering practical reference values.

Black holes merger and the state-of-art detections

Black hole mergers are fascinating astronomical phenomena that occur when two black holes gradually merge as they rotate and approach each other. This phenomenon is caused by explosive gravitational waves that propagate through space until they are detected. Black hole mergers are an extremely powerful origination as well as signature for GWs that can help us better understand the nature of the universe. Using the technology of VLBI, humans can construct the EHT and get access to the image of black holes. The EHT can resolve objects about 4000 times better than the Hubble Space Telescope thanks to this degree of precision, which enables people to perform more precise research on the exploration of the universe. At present, many research institutions around the world are involved, and a huge observation network has been established. Looking ahead, this paper finds that although humans have taken the first-ever photos of the apparent boundaries of black holes, there are still limitations to black hole detection. In future research, researchers will delve into black holes formation and evolution to reveal more about their mysteries. In addition, scientists should explore the interaction of black holes with other celestial bodies, which will help us to better understand galaxy creation and development and provide novel perspectives for the development of cosmology.

I-OPC: An intelligent optimal path computation system using critical path prediction and deep learning for a time-sensitive network

Latency and energy are critical issues when working with time and power-constrained wireless sensor networks. To avoid wasting both time and energy, such systems require selecting optimal communication routes with minimum latency and energy. The energy and latency costs between sender and destination nodes are greatly affected by the occurrence of transmission holes (black hole and grey hole). Therefore, selecting the optimal path must consider the probability of transmission holes and investigate their impact on energy and latency costs. Based on these problems of silent failures, the current work, proposes i-OPC as an intelligent and effective system to address these problems by forecasting sources of such silent failures and resolving them before they occur. The proposed method uses a customized routing schedule and a multi-objective mathematical optimization approach to rank all candidate paths between the source and destination nodes. In addition, i-OPC implements a machine learning technique using deep learning to predict the energy and latency costs of the future location for mobile nodes (). Also, it determines if the future locations of mobile nodes can result in black holes. Experiments produces promising results in terms of delay, energy consumption, packet delivery rate and hole detection rate against existing methods.

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.

Too small to fail: characterizing sub-solar mass black hole mergers with gravitational waves

The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a low-mass coalescence to remain within the LIGO frequency band for thousands of seconds. However, it is unclear whether one can confidently measure the properties of a sub-solar mass compact object and distinguish between a sub-solar mass black hole or other exotic objects. To this end, we perform Bayesian parameter estimation on simulated gravitational-wave signals from sub-solar mass black hole mergers to explore the measurability of their source properties. We find that the LIGO/Virgo detectors during the O4 observing run would be able to confidently identify sub-solar component masses at the threshold of detectability; these events would also be well-localized on the sky and may reveal some information on their binary spin geometry. Further, next-generation detectors such as Cosmic Explorer and the Einstein Telescope will allow for precision measurement of the properties of sub-solar mass mergers and tighter constraints on their compact-object nature.

Improving Coronal Hole Detections and Open Flux Estimates

One systematic limitation of solar coronal hole (CH) detection at extreme ultraviolet (EUV) wavelengths is the obscuration of dark regions of the corona by brighter structures along the line of sight. Another problem arises when using CHs to compute the Sun’s open magnetic flux, where surface measurements of the radial magnetic field, , are situated slightly below the effective height of coronal EUV emission. In this paper, we explore these two limitations utilizing a thermodynamic magnetohydrodynamic (MHD) model of the corona for Carrington rotation (CR) 2101, where we generate CH detections from EUV 193 Å images of the corona forward-modeled from the MHD solution, and where the modeled open field is known. We demonstrate a method to combine EUV images into a full Sun map that helps alleviate CH obscuration called the minimum intensity disk merge (MIDM). We also show the variation in measured open flux and CH area that is due to the effective height differences between EUV and measurements. We then apply the MIDM method to SDO/AIA 193 Å observations from CR 2101, and conduct an analogous analysis. In this case, the MIDM method uses time-varying images, the effects of which are discussed. We show that overall, the MIDM method and an appreciation of the effective height mismatch provide a useful new way to extract a broader view of CHs, especially near the poles. In turn, they enable improved estimates of the open magnetic flux, and help facilitate comparisons between models and observations.

Enabling edge computing-based coverage hole detection framework for lossless data tracking

<p>Countries throughout the world were experimenting with novel approaches to prevent the spreading of the Coronavirus pandemic illness (COVID-19). The use of IoT and cloud computing for data collection and analysis to avoid disease transmission via smartphone applications was a significant hurdle. Existing cloud services that are that retain the information of the victim to address severe challenges such as excessive latency and poor spectral performance. Furthermore, the likelihood of coverage gaps might result in the loss of genuine data, leading the technology to present inaccurate data. Motivated to solve these challenges, this paper addresses a new edge computing framework with a coverage hole detection module to detect and prevent the primary spread of pandemics like COVID-19 in an energy-efficient way. Experimental results exhibit excellent performance in terms of energy consumption under edge based and cloud based scenarios in the existence of coverage holes. The experimental findings demonstrate that the proposed structure has improved energy economy and reduced time to process while detecting coverage holes accurately.</p>

Black Holes in Globular Clusters

Black holes are the laboratory for extreme physics research in the universe, and the detection of black holes is one of the most cutting-edge international scientific goals at present. Globular clusters are old, self-gravity-bound object systems in the Milky Way that contain millions of stars with a high stellar densities inside. Hundreds of pulsar objects have been observed in globular clusters, but the existence of black holes in globular clusters remains an unsolved mystery. A globular cluster may have an intermediatemass black hole at its centre, with masses ranging from 1, 000 to 10, 000 times solar masses. At the same time, there may be a large number of stellar black holes in globular clusters, which will affect the evolution of globular clusters and form. some special objects in globular clusters, which can be observed and confirmed by astronomical observation equipment. This paper will focus on the possible observational features of the presence of black holes in globular clusters and their effects on the properties of globular clusters. Black holes are coupled with their host globular clusters through dynamics, which are important sites for the formation of black hole X-ray binaries and gravitational wave sources, and black holes change the dynamical structure and evolutionary processes of their host clusters. Therefore, searching for X-ray sources, detecting gravitational wave signals, and studying the characteristics of globular clusters are important astronomical evidence for detecting black holes in globular clusters.

Proposed Naïve Bayes- Genetic algorithm to detect black hole attacks in MANETs.

The threats and attacks on mobile ad hoc networks (MANETs) are significant, making it difficult for traditional security systems to provide complete protection. Therefore, an efficient hybrid clustering approach must be designed to construct an intrusion detection system (IDS) that is suitable for this network. IDSs are crucial in MANETs due to the presence of black hole threats, which are the most significant vulnerabilities in this network.Our suggestion is to use a hybrid classifier to detect black hole attacks in MANETs. This can be achieved by using the Naïve Bayes algorithm for clustering to select the cluster head, and modify the Genetic Algorithm to identify the node responsible for a black hole attack on the optimal path. Finally, the confidence server instructs the destination node. If permitted, it alerts the collection head; otherwise, it identifies the node as a malicious one in the black hole attack within each cluster. The results of our proposed technique show that it has improved package damage rate, quantity, package distribution ratio, whole network interruption, and standardized directing capacity parameters compared to current black hole detection approaches.
 The simulation was done in KDD cup 99 to carry out black hole attack and trace file obtained is used as dataset for training and testing purpose using visual basic.