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Automated Stand-alone Surgical Safety Evaluation for Laparoscopic Cholecystectomy (LC) using Convolutional Neural Network and Constrained Local Models (CNN-CLM)
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In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like identifying the sequence of events in the Laparoscopic Cholecystectomy (LC). This study will contribute to show the effectiveness of CNN-CLM approach on laparoscopic cholecystectomy, which will frequently focus on surgical computer vision analysis of surgical safety and related applications. The method of study is deep learning based CNN-CLM to better detect nominal safety as well as unsafe practices around the critical view of safety and AI-based grading scale. The general design flow of AI-recognition of surgical safety is firstly collecting safety surgical videos for frame segmenting and phase according to the image context by surgeon reviewer by CNN-CLM. For this advance research, the dataset is splatted into three main parts where 70% of which is used for training, 15% of which is used for testing and the rest for the cross validation, to achieve the accuracy up to 98.79% of this specific research.  For result part, different metrics of CNN-CLM to evaluate the performance of the proposed model of safety in surgery. The study uses one of the top three performing methods CNN-CLM for the evaluation yields and anatomical structures in laparoscopic cholecystectomy surgery.

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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Safety And Security Engineering
Urban Safety is a Tool for Containing Slums to Reach a Sustainable Urban Structure
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Many cities suffer from the large spread of slums, especially the cities of the Middle East. The purpose of the paper is to study the reality of informal housing in Al-Barrakia and the most important problems that it suffers from. The paper also seeks to study the presence or absence of a correlation between urban safety indicators and urban containment indicators as one of the methods of developing and planning cities. This can be achieved through sustainable urban management. The slums are a source of many urban problems that threaten the security and safety of the residents and represent a focus for the concentration of crimes and drugs. The paper seeks to answer the following question: How can urban safety be improved through urban cont

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Publication Date
Fri Sep 27 2019
Journal Name
Journal Of Baghdad College Of Dentistry
Surgical Treatment Outcome of Salivary Gland Tumors
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Background: Salivary gland neoplasms constitute a group of heterogeneous lesions with complex clinicopathologic characteristics and distinct biological behavior. Numerous studies have suggested geographical variation, therefore the aims of this study were to analyze the characteristics of salivary gland neoplasms in two Iraqi centers and to analyze the postoperative complications that are encountered after surgical treatment of these tumors. Materials and Methods: A retrospective study of the patients who were treated for major and minor epithelial salivary gland tumors was conducted. The analyzed data included; demographic information (age and gender), the site of the tumor, the clinical manifestations, the histological type of the tumo

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Publication Date
Fri Aug 12 2022
Journal Name
Future Internet
Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr

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Publication Date
Sun Oct 05 2025
Journal Name
Mesopotamian Journal Of Computer Science
DGEN: A Dynamic Generative Encryption Network for Adaptive and Secure Image Processing
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Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pix

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Publication Date
Mon Jun 30 2014
Journal Name
Al-kindy College Medical Journal
Effectiveness and safety of non-fixation method in cochlear implantation
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ABSTRACTBackground: cochlear implants are electronic devices that convert sound energy into electrical signals to stimulate ganglion cells and cochlear nerve fibers. These devices are indicated for patients with severe to profound sensorineural hearing losses who receive little or no benefit from hearing aids. The implant basically takes over the function of the cochlear hair cells. The implant consists of external components (microphone, speech processor and transmitting coil) and internal components (receiver stimulator and electrode array). The implant is inserted via a trans mastoid facial recess approach to the round window and scala tympani.Objectives: to determine the effectiveness and safety of non fixation method in cochlear imp

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Publication Date
Wed Apr 01 2020
Journal Name
Journal Of Construction Engineering And Management
Development and Application of Safety Technology Adoption Decision-Making Tool
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Publication Date
Mon Oct 14 2024
Journal Name
Urolithiasis
The safety and efficacy of miniaturized percutaneous nephrolithotomy in children
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To assess the use of miniaturized percutaneous nephrolithotomy (mini-PCNL) for renal stones in children, as well as its safety and efficacy. Seventy-seven patients with more than 15 mm renal stones whose age was less than 15 years were enrolled in this prospective case-controlled study at Al-Ramadi Teaching Hospital, Ar Razi Private Hospital, and Ghazi Al-Hariri Hospital for Surgical Specialties, Anbar and Baghdad, Iraq. The study was conducted from January 2020 to January 2024. The group mentioned above served as group A, and it was compared to the control group (group B), which consisted of 70 adult patients aged 18–60 years. Patients in both groups underwent mini-PCNL. Gender, stone size and location, time of operation, stone-free rate

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Publication Date
Wed Oct 17 2018
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
ESTIMATION OF MUNICIPAL SOLID WASTE GENERATION AND LANDFILL VOLUME GENERATION AND LANDFILL VOLUME USING ARTIFICIAL NEURAL NETWORKS
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Publication Date
Sun Jul 01 2018
Journal Name
International Journal Of Engineering Research And Management
The first and Second Order Polynomial Models with Double Scalar Quantization for Image Compression
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Publication Date
Tue Apr 02 2024
Journal Name
Engineering, Technology & Applied Science Research
Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen

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