Dormount Hope

Until recently, deer hunting in medieval Scotland has been poorly researched archaeologically. In Hunting and Hunting Reserves in Medieval Scotland Gilbert identified medieval parks at Stirling and Kincardine in Perthshire that William the Lion created, but it is only in recent years that excavations by Hall and Malloy have begun to explore their archaeology. The Royal Commission on the Ancient and Historical Monuments of Scotland recorded another type of hunting feature, a deer trap at Hermitage Castle, in 1996 and then re-recorded the earthwork at Dormount Hope in 2000, originally reported as two separate monuments. Although the earthworks of parks and traps display similarities in the construction of their earthwork boundaries, the individual sites have variations in their topography that beg questions about their function. This paper establishes that the earthwork is indeed a single monument which has an open end allowing deer to be driven into the natural canyon of Dormount Hope. It goes on to discuss its dating in both archaeological and documentary terms and then its function as either a park, trap or hay (haga OE). This last possibility is raised by its apparent mention in a Melrose Abbey charter of the neighbouring estate of Raeshaw dating to the last quarter of the 12th century, made by the lords of Hownam, a family of Anglian origin. This Anglian connection leads to its interpretation as a hay – a kind of deer hunting enclosure or trap known in many parts of England prior to the Norman Conquest, for which ‘hay’ place names, such as Hawick, in the Scottish Borders provide support.

Hunter Concerns and Intention to Hunt in Forested Areas Affected by Wildlife Disease

Deer hunting is a major forest-based recreation activity in the US South. However, the recent discovery of chronic wasting disease (CWD) threatens deer hunting in the region. Stakeholders are interested in understanding how hunters perceive the risk and change their hunting behavior. This study found a significant change in hunters’ concerns after the first deer season since the discovery of CWD in Tennessee, USA. Results also showed that hunters’ short- and long-term intentions to hunt deer in the region were positively related to previous experience of hunting in CWD-affected areas, beliefs in the effectiveness of herd reduction to control CWD, concerns regarding potential decline in deer quality and changes in hunting regulations due to CWD, and trust in wildlife agency action. Hunters who hunt on public land and were concerned with deer and human health risk were less likely to hunt in the CWD region. These results are useful in understanding hunter behavior in response to wildlife disease and identifying variables that may help project immediate as well as long-term change in hunting demand in affected regions. Study Implications As two-thirds of forestlands in the USA are under private ownership and public hunting lands are limited or crowded in many regions, deer hunting occurs mostly on private lands. Managers of private and public forestlands that provide recreation access for hunting benefit from a better understanding of how wildlife diseases affect user perception and demand for deer hunting on their lands. One such disease issue that has threatened the hunting industry in the nation is chronic wasting disease in white-tailed deer. Results from this study inform on how hunters perceive the risk of disease, how their relative tolerance changes over time, and what factors determine their intention to hunt in forests with diseased deer. These findings are useful in understanding hunter’s behavior in response to wildlife disease in forest lands and highlight variables that may determine hunting demand in affected regions both in the short- and long-term.

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm

Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.

Feedback deer hunting optimization algorithm for intrusion detection in cloud based deep residual network

Cloud computing is the distributed computing paradigm continually exposed to different attacks and threats of various origins. The data stored in the cloud framework is easier for external and internal intruders, as access to the cloud framework is done through internet services. Various intrusion detection (ID) methods are developed to detect network intruders in the cloud, but these methods are not primarily effective in generating accurate detection results. Hence, an effective intrusion detection system (IDS) is designed to solve the security issues that unfavorably influence the sustainable development of the cloud and enhance the protection of the cloud from malicious attacks. The IDS is modeled using the proposed Feedback Deer Hunting Optimization (FDHO)-based Deep Residual network to detect network intrusions. However, the proposed FDHO algorithm is designed by integrating Feedback Artificial Tree (FAT) with Deer Hunting Optimization (DHOA), respectively. Moreover, the detection of malicious attacks is carried out using a Deep Residual network that significantly increases the training speed, reduces the computational complexity, and generates effective detection results. The performance of the proposed method is comparatively analyzed with the existing techniques, such as Stacked Contractive Auto-Encoder and Support Vector Machine (SCAE+SVM), Artificial Neural Network with ant bee colony optimization algorithm+fuzzy clustering (ANN+ABC+fuzzy clustering), Improved dynamic immune algorithm (IDIA), and Normalized K-means (NK) clustering algorithm with RNN named, (NK-RNN), FAT-based Deep Residual network, and DHOA-based Deep Residual network using the BoT-IoT dataset and KDD cup-99 dataset. The proposed method achieved outstanding performance by considering the metrics, like specificity, accuracy, and sensitivity, with the values of 0.9526, 0.9498, and 0.9214 using the BoT-IoT dataset.

Secured Message Broadcasting in VANET using Blowfish Algorithm with Oppositional Deer Hunting Optimization

Emergency Message broadcasting is an important process in VANET. Security and reliable transmission are the two major concerns in message broadcasting. VANET is open to unauthorized nodes, hackers, misbehaving vehicles, malicious attackers etc without security. Without valid confirmation of authorized vehicles, these types of attacks may occur. To enhance the reliability in message broadcasting, some existing techniques are used. They transmit the data without much delay but they didn’t provide any trusted authentication. So hackers, malicious nodes, unauthorized vehicles may easily interrupt the emergency messages. Also Brute force attack, Man in Middle attack are not identified and eliminated. In this research, a trust based secured broadcasting mechanism is developed which ensures the metrics such as security, privacy, integrity, trust etc. The major intension of this research is to reduce latency and provide high reliable, secure and efficient communication over the vehicles. The data such as vehicle position, location, speed, and other traffic information’s are generated and stored in a separate table. A network is created with varying densities. A path is generated for message broadcasting between source and destination based on the specific gateway estimated. Here, Optimal Wireless Access in Vanet (OWAV) Protocol is employed to gather vehicle related information to reduce the delay. Blowfish encryption algorithm along with Oppositional Deer Hunting Optimization (ODHO) is used to store the trusted vehicles location to avoid unauthorized tracking. The performance of the proposed research is analyzed with various metrics such as Packet delivery ratio (PDR), transmission delay, encryption time, throughput, computational overhead etc. The efficiency of the research is compared with other existing methods.