Journal articles: 'Cross-site scripting; Deep learning; Real-time detection'

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Ayo, Isaac Odun, Williams Toro Abasi, Marion Adebiyi, and Oladapo Alagbe. "An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2442–53. http://dx.doi.org/10.11591/eei.v10i5.3168.

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Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

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Tenzin Yarphel and Diksha Rani. "Cross-Site Scripting (XSS) in Web Applications: A systematic literature review." International Journal of Science and Research Archive 15, no. 2 (2025): 1658–67. https://doi.org/10.30574/ijsra.2025.15.2.1521.

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Cross-Site Scripting (XSS) continues to be a prevalent and damaging vulnerability in web applications, leading attackers to inject harmful scripts that can put personal data at risk, hijack sessions, and change website content. This research provides a comprehensive literature overview of XSS attacks that classify them as stored, reflected, and DOM-based, and discuss how these attacks have evolved as web technology advanced. Traditional detection methods such as input validation and signature-based filters are becoming less and less effective against sophisticated, evasive payloads. As a result, researchers are beginning to utilize Machine Learning (ML) and Deep Learning (DL) methods as more adaptive and intelligent detection methods. This paper reviews different ML/DL models for XSS detection and examines their methods, datasets, feature engineering methods, and metrics for performance. Also pointed out are significant problems such as class imbalance, adversarial examples, and deployment barrier. This study combines current research so that gaps can be identified and future directions described to build effective, scalable, and real-time XSS detection systems. The study also points out that intelligent automation is crucial in protecting web applications against the increasingly sophisticated threat landscape.

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Oshoiribhor, Emmanuel, and Adetokunbo John-Otumu. "XSS-Net: An Intelligent Machine Learning Model for Detecting Cross-Site Scripting (XSS) Attack in Web Application." Machine Learning Research 10, no. 1 (2025): 14–24. https://doi.org/10.11648/j.mlr.20251001.12.

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This research paper focuses on detecting Cross-Site Scripting (XSS) attacks, a prevalent web security threat where attackers inject malicious scripts into web applications to steal sensitive user data, hijack sessions, and execute unauthorized actions. Traditional rule-based and signature-based detection methods often fail against sophisticated and obfuscated XSS payloads, necessitating more advanced solutions. To address this, a machine learning-based model is developed to enhance XSS detection accuracy while minimizing false positives. The proposed approach utilizes feature extraction techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and n-grams, to analyze JavaScript payloads, while Principal Component Analysis (PCA) is employed for feature selection, reducing dimensionality and improving computational efficiency. A Logistic Regression classifier is trained on an XSS payload dataset from Kaggle, with data split into 80% for training and 20% for testing to ensure a robust evaluation. Hyperparameter tuning is performed using GridSearchCV, optimizing the model’s predictive capabilities. Experimental results demonstrate a 99.70% accuracy, with 100% recall and 99.36% precision, highlighting the model’s effectiveness in detecting XSS attacks while minimizing false alarms. The high recall score ensures all malicious payloads are identified, while the strong precision rate enhances reliability for real-world deployment. These findings underscore the potential of machine learning in strengthening web security frameworks, offering a scalable and efficient alternative to conventional detection systems. Future research should focus on enhancing resilience against adversarial attacks by integrating deep learning models such as Bidirectional LSTMs (BiLSTMs) and Transformer-based architectures. Additionally, deploying the model in real-time web security solutions could provide proactive defense mechanisms, ensuring robust protection against evolving XSS threats.

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Kalyan Manohar, Immadisetti, Dadisetti Vishnu Datta, and Lekshmi S. Raveendran. "Website Vulnerability Scanning System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43079.

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With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience

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Niharika Prasanna Kumar. "An Integrated Framework for Securing Web Applications: Machine Learning-Driven XSS Detection and Network-Level Threat Mitigation." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1697–710. https://doi.org/10.52783/jisem.v10i3.8315.

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This paper presents a comprehensive framework designed to address the growing need for securing web-based platforms in today's digital age, where the internet is integral to everyday life. The increasing complexity of cyber threats necessitates advanced solutions, prompting the development of a robust framework that focuses on detecting and mitigating vulnerabilities within web applications. Specifically, this framework targets Cross-Site Scripting (XSS) vulnerabilities and inadequate HTTP header configurations, along with providing protection against SQL injection attacks. The proposed approach leverages state-of-the-art machine learning (ML) algorithms to enable proactive threat detection, enhancing the capability of organizations to identify and neutralize XSS attacks effectively. Furthermore, the framework incorporates real-time network protection mechanisms, exemplified by the integration of the pfSense firewall, to mitigate threats at the network level preemptively. This holistic approach to web security not only reinforces organizational resilience but also ensures compliance with regulatory standards and best practices, thereby reducing the risk of non-compliance and enhancing stakeholder trust. Overall, this framework represents a significant advancement in fortifying the security posture of web-based systems, enabling organizations to navigate the evolving threat landscape confidently and protect critical services and sensitive information.

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Arvind Kamboj, Chandrashekhar Moharir, and Shiva Kiran Lingishetty. "Securing Web Applications Against SQL Injection and XSS Attacks." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 5 (2025): 203–8. https://doi.org/10.51583/ijltemas.2025.140500025.

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Abstract: This paper presents a comprehensive approach to enhancing web application security by mitigating two of the most prevalent and dangerous threats: SQL Injection (SQLi) and Cross-Site Scripting (XSS) attacks. Traditional defense mechanisms such as Web Application Firewalls (WAFs) and rule-based filtering often fall short due to their static nature and limited adaptability to novel or obfuscated attack vectors. To address these shortcomings, the proposed methodology integrates machine learning-based models trained on diverse datasets to accurately detect and classify malicious inputs. Extensive experiments were conducted in both controlled and real-time environments, evaluating the system’s performance using key metrics including accuracy, precision, recall, and F1 score. The results demonstrate that the machine learning model significantly outperforms traditional methods, achieving a detection accuracy of 96.4%, with high precision and recall values, thus offering both effectiveness and efficiency. The system also exhibits scalability and adaptability, making it suitable for deployment in live web applications. This research highlights the critical role of intelligent, data-driven systems in modern cybersecurity frameworks and establishes a strong foundation for future work focused on developing proactive and resilient web application defenses.

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Cheng, Hui, Yonghui Zhao, and Kunwei Feng. "Subsurface Cavity Imaging Based on UNET and Cross–Hole Radar Travel–Time Fingerprint Construction." Remote Sensing 17, no. 12 (2025): 1986. https://doi.org/10.3390/rs17121986.

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As a significant geological hazard in large–scale engineering construction, deep subsurface voids demand effective and precise detection methods. Cross–hole radar tomography overcomes depth limitations by transmitting/receiving electromagnetic (EM) waves between boreholes, enabling the accurate determination of the spatial distribution and EM properties of subsurface cavities. However, conventional inversion approaches, such as travel–time/attenuation tomography and full–waveform inversion, still face challenges in terms of their stability, accuracy, and computational efficiency. To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. A large synthetic dataset of irregular subsurface cavity models is used to pre–train a UNET model, enabling it to learn nonlinear mapping, from fingerprints to velocity structures. To enhance real–world applicability, transfer learning (TL) is employed to fine–tune the model using a small amount of field data. The refined model is then tested on cross–hole radar datasets collected from a highway construction site in Guizhou Province, China. The results demonstrate that the method can accurately recover the shape, location, and extent of underground cavities, outperforming traditional tomography in terms of clarity and interpretability. This approach offers a high–precision, computationally efficient solution for subsurface void detection, with strong engineering applicability in complex geological environments.

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Chang, Fengtian, Guanghui Zhou, Kai Ding, et al. "A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies." Mathematics 11, no. 22 (2023): 4570. http://dx.doi.org/10.3390/math11224570.

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Resistance spot welding poses potential challenges for automotive manufacturing enterprises with regard to ensuring the real-time and accurate quality detection of each welding spot. Nowadays, many machine learning and deep learning methods have been proposed to utilize monitored sensor data to solve these challenges. However, poor detection results or process interpretations are still unaddressed key issues. To bridge the gap, this paper takes the automotive bodies as objects, and proposes a resistance spot welding quality online detection method with dynamic current and resistance data based on a combined convolutional neural network (CNN), long short-term memory network (LSTM), and an attention mechanism. First, an overall online detection framework using an edge–cloud collaboration was proposed. Second, an online quality detection model was established. In it, the combined CNN and LSTM network were used to extract local detail features and temporal correlation features of the data. The attention mechanism was introduced to improve the interpretability of the model. Moreover, the imbalanced data problem was also solved with a multiclass imbalance algorithm and weighted cross-entropy loss function. Finally, an experimental verification and analysis were conducted. The results show that the quality detection accuracy was 98.5%. The proposed method has good detection performance and real-time detection abilities for the in-site welding processes of automobile bodies.

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Cheng, Ming-Fang, Arvind Mukundan, Riya Karmakar, Muhamed Adil Edavana Valappil, Jumana Jouhar, and Hsiang-Chen Wang. "Modern Trends and Recent Applications of Hyperspectral Imaging: A Review." Technologies 13, no. 5 (2025): 170. https://doi.org/10.3390/technologies13050170.

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Hyperspectral imaging (HSI) is an advanced imaging technique that captures detailed spectral information across multiple fields. This review explores its applications in counterfeit detection, remote sensing, agriculture, medical imaging, cancer detection, environmental monitoring, mining, mineralogy, and food processing, specifically highlighting significant achievements from the past five years, providing a timely update across several fields. It also presents a cross-disciplinary classification framework to systematically categorize applications in medical, agriculture, environment, and industry. In counterfeit detection, HSI identified fake currency with high accuracy in the 400–500 nm range and achieved a 99.03% F1-score for counterfeit alcohol detection. Remote sensing applications include hyperspectral satellites, which improve forest classification accuracy by 50%, and soil organic matter, with the prediction reaching R2 = 0.6. In agriculture, the HSI-TransUNet model achieved 86.05% accuracy for crop classification, and disease detection reached 98.09% accuracy. Medical imaging benefits from HSI’s non-invasive diagnostics, distinguishing skin cancer with 87% sensitivity and 88% specificity. In cancer detection, colorectal cancer identification reached 86% sensitivity and 95% specificity. Environmental applications include PM2.5 pollution detection with 85.93% accuracy and marine plastic waste detection with 70–80% accuracy. In food processing, egg freshness prediction achieved R2 = 91%, and pine nut classification reached 100% accuracy. Despite its advantages, HSI faces challenges like high costs and complex data processing. Advances in artificial intelligence and miniaturization are expected to improve accessibility and real-time applications. Future advancements are anticipated to concentrate on the integration of deep learning models for automated feature extraction and decision-making in hyperspectral imaging analysis. The development of lightweight, portable HSI devices will enable more on-site applications in agriculture, healthcare, and environmental monitoring. Moreover, real-time processing methods will enhance efficiency for field deployment. These improvements seek to enhance the accessibility, practicality, and efficacy of HSI in both industrial and clinical environments.

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Ma, Long, Tao Zhou, Baohua Yu, Zhigang Li, Rencheng Fang, and Xinqi Liu. "Improving YOLOv7 for Large Target Classroom Behavior Recognition of Teachers in Smart Classroom Scenarios." Electronics 13, no. 18 (2024): 3726. http://dx.doi.org/10.3390/electronics13183726.

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Deep learning technology has recently become increasingly prevalent in the field of education due to the rapid growth of artificial intelligence. Teachers’ teaching behavior is a crucial component of classroom teaching activities, and identifying and examining teachers’ classroom teaching behavior is an important way to assess teaching. However, the traditional teaching evaluation method involves evaluating by either listening to the class on-site or playing back the teaching video afterward, which is a time-consuming and inefficient manual method. Therefore, this paper obtained teaching behavior data from a real smart classroom scenario and observed and analyzed the teacher behavior characteristics in this scenario. Aiming at the problems of complex classroom environments and the high similarity between teaching behavior classes, a method to improve YOLOv7 for large target classroom behavior recognition in smart classroom scenarios is proposed. First, we constructed the Teacher Classroom Behavior Data Set (TCBDS), which contains 6660 images covering six types of teaching behaviors: facing the board (to_blackboard, tb), facing the students (to_student, ts), writing on the board (writing, w), teaching while facing the board (black_teach, bt), teaching while facing the students (student_teach, st), and interactive (interact, i). This research adds a large target detection layer to the backbone network so that teachers’ instructional behaviors can be efficiently identified in complex classroom circumstances. Second, the original model’s backbone was extended with an effective multiscale attention module (EMA) to construct cross-scale feature dependencies under various branches. Finally, the bounding box loss function of the original model was replaced with MPDIoU, and a bounding box scaling factor was introduced to propose the Inner_MPDIoU loss function. Experiments were conducted using the TCBDS dataset. The method proposed in this study achieved mAP@.50, mAP@.50:.95, and recall values of 96.2%, 82.5%, and 92.9%, respectively—improvements of 1.1%, 2.0%, and 2.3% over the original model. This method outperformed other mainstream models compared to the current state of the art. The experimental results demonstrate the method’s excellent performance, its ability to identify various classroom behaviors of teachers in realistic scenarios, and its potential to facilitate the analysis and visualization of teacher classroom behaviors.

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Leung, P., E. Lester, A. G. Doumouras, et al. "2015 Canadian Surgery Forum02 The usefulness and costs of routine contrast studies after laparoscopic sleeve gastrectomy for detecting staple line leaks03 The association of change in body mass index and health-related quality of life in severely obese patients04 Inpatient cost of bariatric surgery within a regionalized centre of excellence system05 Regional variations in the public delivery of bariatric surgery: an evaluation of the centre of excellence model06 The effect of distance on short-term outcomes after bariatric surgery07 The role of preoperative upper endoscopy in bariatric surgery: a systematic review08 Outcomes of a dedicated bariatric revision surgery clinic10 Quality of follow-up: a systematic review of the research in bariatric surgery14 Bariatric surgery improves weight loss and cardiovascular disease compared with medical management alone: an Alberta multi-institutional early outcomes study16 Diabetic control after laparoscopic gastric bypass and sleeve gastrectomy: a short-term prospective study17 Knowledge and perception of bariatric surgery among primary care physicians: a survey of family doctors in Ontario19 Is early discharge of patients post laparoscopic sleeve gastrectomy safe?22 A comparison of outcomes between bariatric centres of excellence within Ontario02 Closure methods for laparotomy incisions: a cochrane review03 Closing the audit cycle: Are we consenting correctly now?05 Regional variation in the use of surgery in Ontario06 Quitting general surgery residency: attitudes and factors in Canada07 Nipple-sparing mastectomy: utility of intraoperative frozen section analysis of retroareolar tissue08 Withdrawn09 Reliable assessment of operative performance10 Video assessment as a method of assessing surgical competence: the difference in video-rating skills after 4 years of residency11 Burnout among academic surgeons13 Increased health services use by severely obese patients undergoing emergency surgery: a retrospective cohort study14 Novel models for advanced laparoscopic suturing: taking it to the next level16 Pectoral nerve block in breast and axillary surgery17 Predictors for positive resection margins in gastric adenocarcinoma: a population-based analysis18 Predictors of malignancy in thyroid nodules19 Safety and efficacy of POEM for treatment of achalasia: a systematic review of the literature20 Informed consent for surgery21 Meconium ileus: 20 years of experience22 Paraesophageal hernia repair in the elderly: outcomes in a 10-year retrospective study23 The changing face of breast cancer: younger age and aggressive disease in Filipino Canadians24 A systematic review of intraoperative blood loss estimation methods for major noncardiac surgery: a 50-year perspective25 The AVATAR trial: applying vacuum to accomplish reduced wound infections in laparoscopic pediatric surgery27 Indications for use of damage control surgery in civilian trauma patients: a content analysis and expert appropriateness rating study28 Indications for use of thoracic, abdominal, pelvic, and vascular damage control interventions in trauma patients: a content analysis and expert appropriateness rating study29 The impact of health care contact and invasive procedures on Staphylococcus aureus bacteremia: a 5-year retrospective cohort study30 Acute care surgery — positive impact on gallstone pancreatitis31 Safety and efficacy of a step-up approach to management of severe, refractory Clostridium difficile infection32 Clinical and operative outcome of patients with acute cholecystitis who are treated initially with image-guided cholecystostomy34 Assessment of preoperative carbohydrate loading and blood glucose concentration in patients with diabetes35 Impact of pre-emptive lidocaine infiltration at trocar sites (PLITS) and intraoperative ketorolac administration on postoperative pain and narcotics consumption after endocholecystectomy: a randomized-controlled trial36 Expert intraoperative judgment and decision-making: defining the cognitive competencies for safe laparoscopic cholecystectomy37 Teaching clinical anatomy to postgraduate surgical trainees38 Investigating the role of TNFR1 in gastric adenocarcinoma peritoneal metastasis39 Selective outcome reporting and publication biases in surgical randomized controlled trials40 Definitive percutaneous management of symptomatic cholelithiasis41 Peer-based coaching: an innovative method to teach faculty an advanced laparoscopic technique42 Improving teaching and learning in the operating room: Does the surgical procedure feedback rubric support learning?43 Withdrawn44 Mislabelling study designs as case–control in surgical literature45 Measured resting energy expenditure in patients with open abdomens: preliminary data of a prospective pilot study46 Open abdomen management and primary abdominal closure in a surgical abdominal sepsis cohort: a retrospective review47 The effect of early mobilization protocols on postoperative outcomes following abdominal and thoracic surgery: a systematic review49 Program directors and trainees attitudes toward the introduction of multi-source feedback as part of surgical residents’ formative assessment process at the University of Calgary: a qualitative study50 Outcomes associated with alternate blunt cerebrovascular injury detection strategies in major trauma patients: a systematic review and meta-analysis51 Assessing the effect of preoperative nutrition on the surgical recovery of elderly patients53 Why is the percentage of medical students selecting a general surgery career different between Canadian medical schools?54 Colorectal cancer patient perspectives of preoperative repeat endoscopy: a qualitative study55 Staphylococcus aureus bacteremia in a pediatric population: a retrospective study in a tertiary-care referral centre56 The impact of postoperative complications on the recovery of elderly surgical patients57 Withdrawn58 The economics of recovery after pancreatic surgery: detailed cost minimization analysis of a postoperative clinical pathway for patients undergoing pancreaticoduodenectomy59 2015 CJS Editor’s Choice Award Recipient: Achalasia-specific quality of life after pneumatic dilation and laparoscopic Heller myotomy with partial fundoplication: a randomized clinical trial60 NSAID use is associated with an increased risk of anastomotic leak after colorectal surgery: results of a frequentist and Bayesian meta-analysis61 Miracles for babies with abnormal lungs: the story of miR-10a and lung development62 Investigating hospital readmissions and unplanned ED visits following general surgical procedures at a tertiary care centre63 Remote FLS testing: ready for prime time64 Contrast blush (CB) significance on computed tomography (CT) and correlation with noninterventional management (NIM) failure for blunt splenic injury (BSI) in children65 Bridging the gap on the surgical ward: enhancing resident–nurse communication through a CUSP pilot project66 A prospective interim analysis of microbiological gene expression profile of Staphyloccocus aureus bacteremia and its clinical implications67 Outcomes of selective nonoperative management of civilian abdominal gunshot wounds: a systematic review and meta-analysis68 Does rater training improve the reliability of surgical skill assessments? A randomized control trial69 Parallel or divergent? The evolution of emergency general surgery service delivery at 3 Canadian teaching hospitals70 Surgeon satisfaction in the era of dedicated emergency general surgery services: a multicentre study74 Withdrawn76 Timing of cholecystectomy after gallstone pancreatitis: Are we meeting the standards?77 Management of traumatic occult hemothorax, a survey of trauma providers in Canada78 Withdrawn01 Extent of lymph node involvement after esophagectomy with extended lymphadenectomy for esophageal adenocarcinoma predicts recurrence: a large North American cohort study02 A randomized comparison of electronic versus handwritten daily notes in thoracic surgery03 Is tissue still the issue? Lobectomy for suspected lung nodules without preoperative or intraoperative confirmation of malignancy04 Incidence of pulmonary embolism and deep vein thrombosis following major lung resection: a prospective multicentre incidence study05 Venous thromboembolism (VTE) prophylaxis in thoracic surgery: a Canadian national delphi consensus survey06 Preoperative chemoradiation therapy v. chemotherapy in patients undergoing modified en bloc esophagectomy for locally advanced esohageal adenocarcinoma: Does radiation add value?07 Comparative outcomes following tracheal resection for benign versus malignant conditions08 Combined clinical staging for resectable lung cancer: clinicopathological correlations and the role of brain MRI10 A retrospective cohort evaluation of non–small cell lung cancer recurrence detection11 Health-related quality of life measure distinguishes between low and high T stages in esophageal cancer12 Transition from multiport to single-port anatomic lung resection is feasible13 Survival rates in patients with N3 esophageal adenocarcinoma treated with neoadjuvant chemotherapy and esophagectomy with en-bloc lymphadenectomy14 Impact of a dedicated outpatient clinic on the management of malignant pleural effusions16 Has the quality of reporting of randomized controlled trials in thoracic surgery improved?17 Clinical features distinguishing malignant from benign esophageal diagnoses in patients referred to an esophageal diagnostic assessment program18 Concordance with invasive mediastinal staging guidelines19 Current lung-protective ventilation strategies may not be protective during one-lung ventilation surgery20 National practice variation in pneumonectomy perioperative care — results from a survey of the Canadian Association of Thoracic Surgeons21 Outcomes after multimodal treatment of esophagogastric neuroendocrine carcinoma: Is there a role for resection?22 Clinical results of treatment for isolated axillary and plantar hyperhidrosis: a single centre experience23 The role of pneumonectomy after neoadjuvant chemotherapy for N2 non–small cell lung cancer24 Time delays in the management of non–small cell lung cancer: a comparison between high-volume designated and low-volume community hospitals25 Regionalization and outcomes of lung cancer surgery in Ontario, Canada26 Robotic pulmonary resection for lung cancer: the first Canadian series01 The effect of early postoperative nonsteroidal anti-inflammatory drugs on pancreatic fistula following pancreaticoduodenectomy02 Laparoscopic ultrasound still has a role in the staging of pancreatic cancer: a systematic review of the literature03 Impact of portal vein embolization on morbidity and mortality of major liver resection in patients with colorectal metastases: experience of a small single tertiary care centre04 A decision model and cost analysis of intraoperative cell salvage during hepatic resection05 The impact of portal pedicle clamping on survival from colorectal liver metastases in the contemporary era of liver resection: a matched cohort study06 Clinical and pathological features of intraductal papillary neoplasms of the biliary tract and gallbladder07 International practice patterns among ALPPS surgeons: Do we need a consensus?08 Omental flaps to protect pancreaticojejunostomy in pancreatoduodenectomy11 Preoperative diagnostic angiogram and endovascular aortic stent placement for appleby resection candidates: a novel surgical technique in the management of locally advanced pancreatic cancer12 Recurrence following initial hepatectomy for colorectal liver metastases: a multi-institutional analysis of patterns, prognostic factors and impact on survival13 The influence of the multidisciplinary cancer conference era on the management of colorectal liver metastases14 Monosegment ALPPS hepatectomy: extending resectability by rapid hypertrophy15 How does simultaneous resection of colorectal liver metastases impact chemotherapy administration?16 Preoperative liver volumetry for surgical planning: a systematic review and evaluation of current modalities17 Surgical planning of hepatic metastasectomy using radiologist performed intraoperative ultrasound21 Surgical resection and perioperative chemotherapy for colorectal cancer liver metastases: a population-based study22 Management and outcome of colorectal cancer (CRC) liver metastases in the elderly: a population-based study23 Outcomes following repeat hepatic resection for recurrent metastatic colorectal cancer: a population-based study24 A clinical pathway after pancreaticoduodenectomy standardizes postoperative care and may decrease postoperative complications25 Significance of regional lymph node involvement in patients undergoing liver resection and lymphadenectomy for colorectal cancer metastases26 NSAID use and risk of postoperative pancreatic fistulas following pancreaticoduodenectomy: a retrospective cohort study27 Minimally invasive HPB surgery in Canada: What are we doing and do we want to do more?28 2015 CJS Editor’s Choice Award Recipient: Predictors of actual survival in resected pancreatic adenocarcinoma: a population-level analysis29 Predictors of receipt of adjuvant therapy following pancreatic adenocarcinoma resection: a population-based analysis30 Effect of surgical wait time on oncological outcomes in periampullary cancer31 Does surgical assist expertise affect resectability in periampullary malignancies?32 The impact of tranexamic acid on fibrinolytic activity during major liver resection33 Colorectal cancer with synchronous hepatic metastases: a national survey of opinions on treatment sequencing and multidisciplinary cooperation34 Outcomes associated with a matched series of patients undergoing sequential resections of colorectal cancer and hepatic metastases compared with synchronous surgical therapy of the primary and hepatic metastases35 The impact of anesthetic inhalational agent on short-term outcomes after liver resection38 The impact of perioperative blood transfusions on posthepatectomy short-term outcomes: an analysis from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP)39 Associations between pancreatic cancer quality indicators and outcomes in Nova Scotia40 Developing a national quality agenda in hepato-pancreato-biliary surgery: key priority areas for study02 Withdrawn03 Histological features and clinical implications of polypropylene degradation04 A rare case of primary hernia of the perineum05 Migration of polypropylene mesh in the development of late complications06 Laparoscopic hernia repair — Has this procedure run its course?07 Mesh materials used for hernia repair: Why do they shrink?08 The role of pure tissue repairs in a tailored concept for inguinal hernia repair09 Recurrent inguinal hernias a persistent problem in hernia surgery: analysis of 14 640 recurrent cases in the German hernia database, Herniamed10 Open circular intra-abdominal ventral herniorrhaphy: a new technique in ventral hernia repair01 Misrepresentation or “spin” is common in robotic colorectal surgical studies02 Postoperative pelvic sepsis rates following complete pathologic response to neoadjuvant therapy in rectal cancer03 Understanding the complexities of shared decision-making in cancer: a qualitative study of the perspectives of patients undergoing colorectal surgery04 Impact of hospital volume on quality indices for rectal cancer surgery in British Columbia, Canada07 The effect of laparoscopy on inpatient cost after elective colectomy for colon cancer08 Predictors of variation in neighbourhood access to laparoscopic colectomy for colon cancer09 Predictors of 30-day readmission after elective colectomy for colon cancer10 Neutrophil-to-lymphocyte ratio predicts major perioperative complications in patients with colorectal cancer12 Sessile serrated adenoma (SSA) detection-predictive factors13 Diverticular abscess managed with long-term definitive nonoperative intent is safe14 Long-term outcomes of conservative management following successful nonoperative treatment of acute diverticulitis with abscess: a systematic review15 Incidence of ischemic colitis after abdominal aortic aneurysm repair: results from the national surgical quality improvement program database16 Sigmoid colectomy for acute diverticulitis in immunosuppressed v. immunocompetent patients: outcomes from the ACS-NSQIP database17 A cross-sectional survey of health and quality of life of patients awaiting colorectal surgery in Canada19 Self-expanding metal stents versus emergent surgery in acute malignant large bowel obstruction20 Combined laparoscopic and TAMIS LAR in a morbidly obese patient after open right hepatectomy21 Safety and feasibility of laparoscopic rectal cancer resection in morbidly obese patients22 Factors associated with morbidity following sacral neurostimulation for fecal incontinence: beware of the high risk groups23 Hyperglycemia increases surgical site infections following colorectal resections for malignancy in a standardized patient cohort24 Implementing an enhanced recovery program after colorectal surgery in elderly patients: Is it feasible?25 From laparoscopic-assisted to total laparoscopic right colectomy with intracorporeal anastomosis: Is the shift in technique justified?26 Surgical site infection rates following implementation of a “colorectal closure bundle” in elective colorectal surgeries27 Quality of life and anorectal function of rectal cancer patients in long-term recovery28 Combined laparoscopic/transanal endoscopic microsurgery approach to radical resection for rectal tumours29 Transanal endoscopic microsurgery resection of rectal neuroendocrine tumours: a single centre Canadian experience30 Abdominoperineal reconstruction with a myocutaneous flap32 Comparison of robotic and laparoscopic colorectal surgery with respect to 30-day perioperative morbidity33 Definitive management of fistula-in-ano using draining setons35 Oncologic outcomes following complete pathologic response to neoadjuvant therapy in rectal cancer36 Laparoscopic total mesorectal excision in obese patients with rectal cancer: What is the oncological impact?38 Improving the enhanced recovery programs in laparoscopic colectomy: liposomal bupivacaine may not be the answer39 Fistulae related to colonic diverticular disease: a single institution experience41 Laparoscopic colectomy for malignancy provides similar pathologic outcomes and improved survival outcomes compared with open approaches42 MRI utilization and completeness of reporting in rectal cancer: a population-based study43 Supporting quality assurance initiatives for rectal cancer: Is the CAP protocol enough?44 Accuracy and predictive ability of preoperative MRI for rectal adenocarcinoma: room for improvement47 A population-based study of colorectal cancer in patients ≤ 40: Does the extent of resection affect outcomes?48 Transanal minimally invasive surgery (TAMIS) for rectal neoplasms01 The impact of blood transfusion on perioperative outcomes following resection of gastric cancer: an analysis of the ACS-NSQIP02 Association of wait time to surgical management with overall survival in Ontarians with melanoma04 General surgeons’ attitudes toward breast reconstruction in the province of Quebec06 Neoadjuvant chemotherapy for breast cancer: Is practice changing? A population-based review of current surgical trends07 Robotic versus laparoscopic versus open gastrectomy for gastric adenocarcinoma15 Influence of preoperative MRI on the surgical management of breast cancer patients17 Adverse events related to lymph node dissection for cutaneous melanoma: a systematic review and meta-analysis19 Regional variations in survival, case volume and intraoperative margin assessment in resected gastric cancer20 Comparison of clinical and economic outcomes between robotic, laparoscopic and open rectal cancer surgery: early experience at a tertiary care centre21 Outcomes and clinicopathologic features of patients with Angiosarcoma of the breast23 Postmastectomy radiation: Should subtype factor in to the decision?24 Omission of axillary staging in elderly patients with early stage breast cancer impacts regional control but not survival: a systematic review and meta-analysis25 Objective pathological assessment of CRCLM by MALDI26 Identification of predictive tumour markers in breast cancer tissue — a pilot study research plan27 Reframing women’s risk: counselling on contralateral prophylactic mastectomy in non–high risk women with early breast cancer28 Withdrawn30 Comparison of different methods of immediate breast reconstructions for breast cancer patients: Is “single stage” really better?32 Is lymph node ratio a more accurate prognostic factor in stage III colon cancer than standard nodal staging?33 Costs associated with reoperation in the setting of attempted breast-conserving surgery: a decision analysis34 Polo-like kinase 4 (Plk4) activates Cdc42, stimulates cell invasion and enhances cancer progression in vivo35 Negative predictive value of preoperative abdominal CT in determining gastric cancer resectability on a population level36 2015 CJS Editor’s Choice Award Recipient: (18)F-fluoroazomycin arabinoside positron emission tomography (FAZA-PET) imaging predicts response to chemoradiation and evofosfamide (TH-302) in a preclinical xenograft model of rectal cancer37 Impact of a regional guideline on the surgical treatment of the axilla in patients with breast cancer: a population-based study39 Recent trends in port-site metastasis following laparoscopic resection of gallbladder cancer: a systematic review40 Real-time electromagnetic navigation for breast tumour resection: pilot study on palpable tumours41 Neoadjuvant imatinib for primary gastrointestinal stromal tumour (GIST): mutational status and timing of resection42 Adherence to osteoporosis screening guidelines in seniors with breast cancer treated with anti-estrogen therapy: a population-based study43 Automated robot interventions for enhanced clinical outcomes in breast biopsy44 Preoperative pregabalin or gabapentin for postoperative acute and chronic pain among patients undergoing breast cancer surgery: a systematic review and meta-analysis of randomized controlled trials46 Uptake and impact of synoptic reporting on breast cancer operative reports in a community care setting47 Withdrawn." Canadian Journal of Surgery 58, no. 4 Suppl 2 (2015): S169—S238. http://dx.doi.org/10.1503/cjs.008615.

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Birk, Manjot, Vivien Chan, Nicholas J. Yee, et al. "Canadian Spine SocietyCPSS-1. Abstract ID 108. Radiographic reporting in adolescent idiopathic scoliosis: Is there a discrepancy between radiologists’ reports and surgeons’ assessments?CPSS-2. Abstract ID 21. Pediatric posterior spinal deformity correction: 30-day postoperative infection rate and risk factorsCPSS-3. Abstract ID 17. “Ultra-low dose” computed tomography without sedation is feasible and should be considered as part of the preoperative optimization pathway in paediatric patients with neuromuscular scoliosisCPSS-4. Abstract ID 20. SeeSpine: a novel surface topography smartphone application for monitoring curve progression in adolescent idiopathic scoliosisCPSS-5. Abstract ID 78. Pilot study: a machine learning algorithm for the detection of adolescent idiopathic scoliosis from images taken with modern smartphone technologyCPSS-6. Abstract ID 101. Preoperative parameters influencing vertebral body tethering outcomes: patient characteristics play an important role in determining the outcomes at 2 years after surgeryCPSS-7. Abstract ID 63. Preoperative bending radiographs are the best predictor of scoliosis correction on the first erect radiograph in vertebral body tethering: a single-centre retrospective studyCPSS-8. Abstract ID 18. Adverse events after zoledronate infusion in medically complex patients with neuromuscular scoliosisCPSS-9. Abstract ID 5. Sequential rod rolling for surgical correction of Lenke type 2 adolescent idiopathic scoliosis: a 3D analysisCPSS-10. Abstract ID 123. A comparative study of protocols for spinal casting as a surgical delay strategy in severe early-onset scoliosisA-11. Abstract ID 50. Does the type of pelvic fixation affect pelvic incidence after adult spinal deformity surgery? A retrospective analysisA-12. Abstract ID 51. How does pelvic fixation affect the compensatory mechanisms after adult spinal deformity surgery? A retrospective analysisA-13. Abstract ID 44. Development of a biomechanical model to identify risk factors in sagittal alignment contributing to proximal junctional kyphosisA-14. Abstract ID 32. Biomechanical characterization of semirigid constructs and the potential effect on proximal junctional kyphosisA-15. Abstract ID 65. Early adjacent disc characteristics are not associated with reoperation in short-segment lumbar fusionsA-16. Abstract ID 39. Concurrent validation of a novel inertial measurement unit–based method to evaluate spinal motion in clinical settingsA-17. Abstract ID 68. Distal lordosis is associated with reoperation for adjacent segment disease in patients with degenerative lumbar fusionA-18. Abstract ID 69. Automatic extraction of spinopelvic parameters using artificial intelligence methods and a review on the effects of spine stiffness, spinal fusion and spinopelvic parameters on lower limb motion and total hip arthroplasty outcomesA-19. Abstract ID 38. Gender differences in fusion rates in the treatment of degenerative lumbar spondylolisthesis: analysis from the CSORN prospective degenerative lumbar spondylolisthesis studyA-20. Abstract ID 29. L3–4 hyperlordosis after a reduction in lower lumbar lordosis with L4–L5 fusion surgery is common in patients requiring L3–4 revision surgery for adjacent segment diseaseB-21. Abstract ID 40. Predictors of dynamic instability in the decision to fuse in degenerative lumbar spondylolisthesis: results from the Canadian Spine Outcomes and Research Network prospective degenerative lumbar spondylolisthesisstudyB-22. Abstract ID 49. Impact of preoperative insomnia on poor postoperative pain control after elective spine surgery and the Modified Calgary Postoperative Pain After Spine Surgery scoreB-23. Abstract ID 115. Influence of high pelvic incidence on operative difficulty in patients treated surgically for degenerative lumbar spondylolisthesisB-24. Abstract ID 45. Reoperation rates for adjacent segment disease in degenerative lumbar fusion surgery: a comparison between minimally invasive versus open surgical approachesB-25. Abstract ID 118. Assessment of changes in opioid utilization 1 year after elective spine surgery: a Canadian Spine Outcomes and Research Network studyB-26. Abstract ID 93. Preoperative neuroleptic and opioid use effects on postoperative pain and disability after spinal surgery for lumbar radiculopathyB-27. Abstract ID 52. The importance of lower extremity compensation mechanisms in lumbar degenerative pathology: a retrospective analysisB-28. Abstract ID 107. Persistent poor sleep is associated with worse pain and quality of life in patients with degenerative thoracolumbar conditions undergoing surgery: a retrospective cohort studyB-29. Abstract ID 126. Opioid use in low back pain is associated with increased utilization of health care services and likelihood of work absenteeismB-30. Abstract ID 53. Wait times for degenerative lumbar spine consultation and surgery: a repeated cross-sectional analysis of the Canadian Spine Outcomes and Research NetworkC-31. Abstract ID 33. Patients with radicular pain improve more than those with axial pain alone after treatment for metastatic spine diseaseC-32. Abstract ID 46. Association between nutritional status and survival in patients requiring treatment for spinal metastasesC-33. Abstract ID 47. Introduction of the new Patient Expectations in Spinal Oncology questionnaireC-34. Abstract ID 74. Medium-term follow-up outcomes in palliative transpedicular corpectomy with cement-based anterior vertebral reconstruction performed for patients with spinal metastasisC-35. Abstract ID 10. Perception of frailty in spinal metastatic disease: international survey of the AO Spine CommunityC-36. Abstract ID 73. COVID-19: Were we able to get back to the prepandemic level of spine surgery activity? An experience from a tertiary referral centre in QuebecC-37. Abstract ID 114. Provider confidence with virtual spine exams 2 years after COVID-19 lockdown restrictionsC-38. Abstract ID 76. The impact of nasal decontamination by photodisinfection in spine surgery: a feasibility pilot studyC-39. Abstract ID 116. Exploring the bacterial hypothesis of low back pain: a prospective cohort studyC-40. Abstract ID 7. Management of deep surgical site infections of the spine: a Canadian surveyD-41. Abstract ID 26. Earlier tracheostomy reduces complications in complete cervical spinal cord injury in real-world practice: analysis of a multicentre cohort of 2001 patientsD-42. Abstract ID 87. Neuroprotection after traumatic spinal cord injury through mitochondrial calcium uniporter inhibitionD-43. Abstract ID 16. The impact of specialized versus nonspecialized acute hospital care on survival among patients with acute incomplete traumatic spinal cord injuries: a population-based observational study from British Columbia, CanadaD-44. Abstract ID 59. Stem cells from human spinal cord exhibit reduced oligodendrogenesis compared with rodent stem cellsD-45. Abstract ID 122. Harnessing the endogenous stem cell response after spinal cord injuryD-46. Abstract ID 62. Comparison of age and 5-Item Modified Frailty Index as predictors of in-hospital mortality for patients with complete traumatic cervical spinal cord injuryD-47. Abstract ID 109. Unplanned readmissions after traumatic spinal cord injury: perspective from the British Columbian populationD-48. Abstract ID 9. The radiographic characteristics that lead surgeons to agree and disagree on making treatment recommendations in thoracolumbar burst fractures without neurologic deficitsD-49. Abstract ID 19. The effect of Enhanced Recovery After Surgery protocols for elective cervical and lumbar spine procedures on hospital length of stay: a systematic review and meta-analysisD-50. Abstract ID 23. Exploring end-of-life decision-making and perspectives on medical assistance in dying through the eyes of individuals living with cervical spinal cord injuries in Nova ScotiaE-51. Abstract ID 88. Neurologically intact thoracolumbar burst fractures (AO Spine A3, A4) improve on Oswestry Disability Index equally when treated surgically versus nonoperativelyE-52. Abstract ID 28. Predictive algorithm for surgery recommendation in thoracolumbar burst fractures without neurological deficitsE-53. Abstract ID 36. A randomized trial of cervical orthosis versus no orthosis after multilevel posterior cervical fusionE-54. Abstract ID 11. Deterioration after surgery for degenerative cervical myelopathy: an observational study from the Canadian Spine Outcomes and Research NetworkE-55. Abstract ID 66. Canadian cohort of older patients with cervical spinal cord injury: Do radiologic parameters correlate with initial neurological impairment?E-56. Abstract ID 6. Surgical complications or neurologic decline? A patient discrete-choice experiment for cervical myelopathyE-57. Abstract ID 82. Laminectomy alone for cervical spondylotic myelopathy: a Canadian Spine Outcomes and Research Network StudyE-58. Abstract ID 95. The effect of surgical approach on patient outcomes of degenerative cervical myelopathy: a pooled analysis of individual patient data from 1031 casesE-59. Abstract ID 81. Occiput and upper cervical fusions: Does navigation matter? A Canadian Spine Outcomes and Research Network studyE-60. Abstract ID 89. Preoperative therapies improve postoperative disability in patients who undergo anterior cervical discectomy and fusion surgery for cervical radiculopathyF-61. Abstract ID 58. The influence of wait time on surgical outcomes in elective lumbar degenerative surgery: a Canadian Spine Outcomes and Research Network studyF-62. Abstract ID 77. A cost consequence analysis comparing spinal fusion versus decompression alone for lumbar degenerative spondylolisthesisF-63. Abstract ID 96. Economic impact of wait time in degenerative lumbar stenosis surgery: association with time away from work, chronic persistent opioid use and patient satisfactionF-64. Abstract ID 121. Optimal timing of surgery for symptomatic single-level lumbar disc herniation: a cost-effectiveness analysisF-65. Abstract ID 67. Impact of scheduled spine surgery for degenerative spinal disorders on patient health-related quality of life compared with the general Canadian populationF-66. Abstract ID 84. Decompression and decompression and fusion and the influence of spinopelvic alignment in the outcome of patients with degenerative lumbar spondylolisthesisF-67. Abstract ID 43. Association between poor postoperative pain control and surgical outcomes after elective spine surgeryF-68. Abstract ID 56. Factors associated with shorter wait times for lumbar degenerative spinal surgeryF-69. Abstract ID 25. Is navigation a game changer in single-level transforaminal lumbar interbody fusions?F-70. Abstract ID 34. Radiologic and clinical evaluation of posterolateral versus interbody fusion in degenerative lumbar spondylolisthesisG-71. Abstract ID 15. Timing of recovery after surgery for patients with degenerative cervical myelopathy: an observational study from the Canadian Spine Outcomes and Research NetworkG-72. Abstract ID 30. Development of a patient-centred cervical myelopathy severity index: measurement property testing, item generation and item reductionG-73. Abstract ID 75. The preoperative expectations of patients with degenerative cervical myelopathyG-74. Abstract ID 61. Satisfaction with surgical treatment for degenerative cervical myelopathy is driven by improvement in patient-reported outcomesG-75. Abstract ID 98. Identification of surgical candidates for mild degenerative cervical myelopathy: a trajectory-based analysisG-76. Abstract ID 100. The impact of surgery on pain in degenerative cervical myelopathy: a pooled analysis of 1047 patients from CSM-North America, CSM-International and CSM-Protect trialsG-77. Abstract ID 104. National adverse event rates after cervical spine surgery for degenerative disorders, and impact on patient satisfactionG-78. Abstract ID 8. The unsustainable growth of out-of-hours emergent surgery for degenerative spinal disease in Canada: a retrospective cohort study from a national registryG-79. Abstract ID 102. Effect of compensation claim status on perioperative outcomes in patients with degenerative spine conditionsG-80. Abstract ID 13. Outcomes of spinal cord stimulation for management of neuropathic pain in patients with spinal cord injuryP-81. Abstract ID 97. Meaningfulness in clinical improvements at 12 months after surgery for degenerative cervical myelopathy: comparison of 30% change versus absolute change values of minimal clinically important differenceP-82. Abstract ID 22. An exploration of the evolving perception of quality of life from the perspective of individuals living with a cervical spinal cord injury in Nova ScotiaP-83. Abstract ID 41. Delays in diagnosis of degenerative cervical myelopathy: a population-based study using the Clinical Practice Research DatalinkP-84. Abstract ID 119. Sex, drugs and spine surgery: a nationwide analysis of opioid utilization and patient-reported outcomes in males and femalesP-85. Abstract ID 117. The feasibility of a multidisciplinary transitional pain service in patients undergoing spine surgery to minimize opioid use and improve perioperative outcomes: a quality improvement studyP-86. Abstract ID 103. Predictors of poor postoperative patient satisfaction in patients undergoing elective spine surgery with pre-existing compensation claimsP-87. Abstract ID 60. The efficacy and safety of P-15 peptide enhanced bone graft in bone regeneration: a systematic reviewP-88. Abstract ID 113. The influence of preoperative back pain on patient-rated outcomes after decompression with or without fusion for degenerative lumbar spondylolisthesis: results from the Canadian Spine Outcomes and Research Network prospective degenerative lumbar spondylolisthesis studyP-89. Abstract ID 55. Publication retraction in spine surgery: a systematic reviewP-90. Abstract ID 12. The use of a standardized surgical case log to document operative exposure to procedural competencies in a spine surgery fellowship curriculum: a university-wide initiativeP-91. Abstract ID 90. Preoperative psychosocial factors affect the outcomes experienced by patients who undergo anterior cervical discectomy and fusion surgery for cervical radiculopathyP-92. Abstract ID 91. Virtual reality for patient-specific, multidisciplinary planning of complex orthopedic oncological surgery including the spineP-93. Abstract ID 35. Malposition in robotic-assisted cortical bone trajectory screw placement: analysis of 1025 consecutive screwsP-94. Abstract ID 79. Accuracy of computer-assisted spine navigation platforms: a meta-analysis of 16 040 screwsP-95. Abstract ID 86. Which is better: percutaneous or open robot-assisted spine surgery? Prospective, multicentre study of 2524 screws in 336 patientsP-96. Abstract ID 124. Opioid use in low back pain is associated with decreased quality of life, increased disability and worse treatment outcomes: a stratified propensity score analysisP-97. Abstract ID 85. Incidence and management of deep spine surgical-site infections: a systematic review and meta-analysisP-99. Abstract ID 110. Associations of preoperative analgesic use with postoperative pain and disability after spinal surgery for cervical myelopathy and radiculopathyP-100. Abstract ID 42. Cervical myelopathy and social media: a mixed-methods analysisP-101. Abstract ID 24. The use of machine learning to predict the presence of cauda equina syndrome among patients with disc herniationP-102. Abstract ID 112. A systematic review of the content and structure of composite end points in spine surgery interventional trialsP-103. Abstract ID 106. Surveying the knowledge and attitudes of moving to a high-quality, low-carbon health care systemP-104. Abstract ID 125. Variability in treatment of adult spinal deformity, a Canadian surveyP-105. Abstract ID 83. Anterior cervical hybrid constructs reduce upper adjacent segment hypermobility compared with anterior cervical discectomy and fusionP-106. Abstract ID 48. A preliminary report of robotic screw insertion in cadaveric vertebrae using the Mazor X systemP-107. Abstract ID 54. Invasive brain–computer interface for motor restoration in spinal cord injury: a systematic reviewP-108. Abstract ID 27. A new cost-effective technique to mimic pedicle screw trajectory in cadavers: a robotic validation studyP-109. Abstract ID 14. Developments and applications of augmented and virtual reality technology in spine surgery training: a systematic reviewP-110. Abstract ID 80. Comprehensive accuracy analysis of robotic models in spine surgery: a pooled analysis of 14 462 screwsP-111. Abstract ID 99. Familial chiari malformation: a systematic reviewP-112. Abstract ID 31. Ninety-day complication and revision surgery rates using navigated robotics in thoracolumbar spine surgeryP-113. Abstract ID 111. Which baseline clinical factors and clinical indications are most correlated with outcome after lumbar fusion surgery?P-114. Abstract ID 92. Characterization of the mechanical state of human mesenchymal stem cells on micro- or nano-textured Ti6Al4V surfacesP-115. Abstract ID 72. Short-term outcomes associated with the use of macro–micro–nano rough Ti6Al4V (nanoLOCK) interbody cages in patients with lumbar spine degenerative conditionsP-116. Abstract ID 127. Introduction of a novel concept to decompress foramen magnum in chiari-1 malformation without affecting stabilityP-117. Abstract ID 57. Minimally invasive tubular lumbar decompression without fusion in lumbar stenosis with underlying deformity: Friend or foe?P-118. Abstract ID 64. The role of intraoperative ultrasound in nononcological intradural lumbar spine conditions: intradural lumbar disc herniation and subdural spinal abscessP-119. Abstract ID 120. Prospective Prophylactic Antibiotics Regimen in Spine Surgery: the PPARiSS cohortP-121. Abstract ID 70. Decompression versus decompression and fusion in cauda equina syndrome secondary to massive lumbar disc herniationP-122. Abstract ID 105. Implementation of robot-assisted surgery for elective spine surgeryP-123. Abstract ID 37. Spine surgery in patients with morbid obesity: tips and tricksP98: Abstract ID 71. Pelvic incidence is associated with reoperation for adjacent segment disease in degenerative lumbar spinal fusion surgery." Canadian Journal of Surgery 66, no. 4 Suppl 1 (2023): S1—S53. http://dx.doi.org/10.1503/cjs.006523.

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Isaac, Odun-Ayo, Toro-Abasi Williams, Adebiyi Marion, and Alagbe Oladapo. "An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning." October 1, 2021. https://doi.org/10.11591/eei.v10i5.3168.

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Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.

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Yan, Huyong, Li Feng, You Yu, et al. "Cross-site scripting attack detection based on a modified convolution neural network." Frontiers in Computational Neuroscience 16 (August 29, 2022). http://dx.doi.org/10.3389/fncom.2022.981739.

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Cross-site scripting (XSS) attacks are currently one of the most threatening network attack methods. Effectively detecting and intercepting XSS attacks is an important research topic in the network security field. This manuscript proposes a convolutional neural network based on a modified ResNet block and NiN model (MRBN-CNN) to address this problem. The main innovations of this model are to preprocess the URL according to the syntax and semantic characteristics of XSS attack script encoding, improve the ResNet residual module, extract features from three different angles, and replace the full connection layer in combination with the 1*1 convolution characteristics. Compared with the traditional machine learning and deep learning detection models, it is found that this model has better performance and convergence time. In addition, the proposed method has a detection rate compared to a baseline of approximately 75% of up to 99.23% accuracy, 99.94 precision, and a 98.53% recall value.

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Parumanchala, Bhaskar. "Detecting Web Attacks with end-to-end Deep Learning." April 25, 2023. https://doi.org/10.5281/zenodo.7894540.

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Web applications are popular targets for cyber-attacks because they are networkaccessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications.This project provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibilityof an unsupervised/semisupervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data. In this project author evaluating propose Auto Encoder Algorithm with SVM and Naïve Bayes. In extension work we are using LSTM algorithm and comparing with all algorithms.

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"Hybrid deep learning based intrusion detection system using Modified Chicken Swarm Optimization algorithm." ARPN Journal of Engineering and Applied Sciences, September 30, 2023, 1707–18. http://dx.doi.org/10.59018/0723212.

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Web Systems which are the backbone of information resources, communications, and personal information management, attackers might take advantage of their vulnerability and beguile them to get access to sensitive data or the web servers and apps in full. Wider usage of the Internet and its features have come a long ago, with both advantages and disadvantages. The security and its side effects have been discussed by researchers in various aspects of the network. HTTP, one of the most widely used network protocols, has paid a huge price due to various intrusions like SQL injections, code injections, and cross-site scripting (XSS). To handle these intrusions, various intrusion detection algorithms have been proposed and addressed in the literature. The accuracy and timeliness of these detections has been an issue due to false positives and the amount of information that ought to be processed and delivered within a short span of time. In recent times the classic classifiers have become outdated and detecting abnormal traffic in a web system has become a hassle. In this research, we propose handling intrusion detection over a Web System using hybrid Deep Learning (HDL) based classification and robust feature extraction using a modified chicken swarm optimization algorithm (MCSO). This approach also incorporates the idea of deep learning, which makes it possible to have a peer-to-peer learning system for aberrant patterns with a fewer number of characteristics, hence reducing the amount of time needed to complete the work. In order to distinguish or classify data that the web system has to deal with, a hybrid computational intelligence-based classifier algorithm is used. The combination of the hybrid classifier is fuzzy neural network with Long Short Term Memory (LSTM) which is basically used to classify the attacks and distinguish between normal and anomalous data. The use of helps in understanding the nature of intrusions over time, which is constant and predictable, On the other hand, with the assistance of deep learning and a method called feature extraction, which pulls important information from noisy data, we can do this. Lastly, the findings of the experiments show that this technique has an excellent detection performance, with an accuracy rate that is more than 98.7%.

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Wang, Qiuhua, Chuangchuang Li, Dong Wang, et al. "IGXSS: XSS payload detection model based on inductive GCN." International Journal of Network Management, February 11, 2024. http://dx.doi.org/10.1002/nem.2264.

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AbstractTo facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross‐site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high‐quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two‐layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.

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Meklati, Safia, Kenza Boussora, Mohamed El Hafedh Abdi, and Sid-Ahmed Berrani. "Surface Damage Identification for Heritage Site Protection: A Mobile Crowd-sensing Solution Based on Deep Learning." Journal on Computing and Cultural Heritage, October 26, 2022. http://dx.doi.org/10.1145/3569093.

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This paper addresses the general problem of built heritage protection against both deterioration and loss. In order to continuously monitor and update the structural health status, a crowd-sensing solution based on powerful and automatic deep learning technique is proposed. The aim of this solution is to get rid of the limitations of manual and visual damage detection methods that are costly and time consuming. Instead, automatic visual inspection for damage detection on walls is efficiently and effectively performed using an embedded Convolutional Neural Network (CNN). This CNN detects the most frequent types of surface damage on wall photos. The study has been conducted in the Kasbah of Algiers where the four following types of damages have been considered: Efflorescence, spall, crack, and mold. The CNN is designed and trained to be integrated into a mobile application for a participatory crowd-sensing solution. The application should be widely and freely deployed, so that any user can take a picture of a suspected damaged wall, and get an instant and automatic diagnosis, through the embedded CNN. In this context, we have chosen MobileNetV2 with a transfer learning approach. A set of real images have been collected and manually annotated, and have been used for training, validation, and test. Extensive experiments have been conducted in order to assess the efficiency and the effectiveness of the proposed solution, using a 5 fold cross validation procedure. Obtained results show in particular a mean weighted average precision of 0.868 ± 0.00862 (with a 99% of confidence level) and a mean weighted average recall of 0.84 ± 0.00729 (with a 99% of confidence level). To evaluate the performance of MobileNetV2 as a feature extractor, we conducted a comparative study with other small backbones. Further analysis of CNN activation using Grad-Cam has also been done. Obtained results show that our method remains effective even when using a small network and medium to low resolution images. MobileNetV2-based CNN size is smaller, and computational cost better, compared to the other CNNs, with similar performance results. Finally, detected surface damages have also been plotted on a geographic map, giving a global view of their distribution.

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Thapaliya, Dr Suman, Mr Ashish Gautam: Ph.D. scholar, and Mr Prabin Khadka: MCS Scholar,. "AI-Driven Web Exploitation with Metasploit." International Journal of Multidisciplinary and Innovative Research 02, no. 06 (2025). https://doi.org/10.58806/ijmir.2025.v2i6n02.

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The integration of artificial intelligence (AI) with the Metasploit framework represents a significant advancement in the field of web application security, transforming how vulnerabilities are identified, exploited, and addressed. As cyber threats continue to grow in complexity and frequency, traditional penetration testing tools like Metasploit, though powerful, often fall short in addressing the dynamic nature of modern web systems. This paper investigates how AI, particularly through machine learning (ML) and natural language processing (NLP), can enhance Metasploit’s capabilities by automating and optimizing key tasks such as vulnerability detection, exploit development, and post-exploitation analysis. By incorporating AI-driven techniques, security professionals can identify vulnerabilities such as SQL injection, cross-site scripting (XSS), and weak authentication mechanisms more accurately and efficiently. Additionally, AI enables the automated generation of tailored exploits, reducing the manual effort and time typically involved in penetration testing. Post-exploitation, AI algorithms can analyze data from compromised systems to uncover additional threats or entry points, thereby enriching the depth and value of security assessments. The integration of AI also introduces possibilities for real-time threat detection and faster response mechanisms, equipping organizations with tools to better anticipate and counteract cyber threats. However, this advancement also raises ethical and practical concerns, including the risk of misuse by malicious actors, potential privacy violations, and the presence of bias within AI models. These challenges highlight the need for responsible AI usage, supported by clear ethical guidelines and regulatory oversight. In conclusion, the fusion of AI with the Metasploit framework offers a promising leap forward in penetration testing, providing improved accuracy, speed, and insight. While the benefits are substantial, it is essential to address the associated ethical and societal implications. This paper aims to provide a comprehensive overview of the opportunities, challenges, and future prospects of AI-powered Metasploit, offering valuable perspectives for researchers, practitioners, and policymakers in the cybersecurity domain.

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Saleem, Muhammad Hammad, Kesini Krishnan Velayudhan, Johan Potgieter, and Khalid Mahmood Arif. "Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms." Frontiers in Plant Science 13 (April 25, 2022). http://dx.doi.org/10.3389/fpls.2022.850666.

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The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

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Saleem, Muhammad Hammad, Kesini Krishnan Velayudhan, Johan Potgieter, and Khalid Mahmood Arif. "Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms." Frontiers in Plant Science 13 (April 25, 2022). http://dx.doi.org/10.3389/fpls.2022.850666.

Full text

Abstract:

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

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Journal articles on the topic 'Cross-site scripting; Deep learning; Real-time detection'

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