The Brescia Internationally Validated European Guidelines on Minimally Invasive Pancreatic Surgery (EGUMIPS).

OBJECTIVE: To develop and update evidence-based and consensus-based guidelines on laparoscopic and robotic pancreatic surgery. SUMMARY BACKGROUND DATA: Minimally invasive pancreatic surgery (MIPS), including laparoscopic and robotic surgery, is complex and technically demanding. Minimizing the risk for patients requires stringent, evidence-based guidelines. Since the International Miami Guidelines on MIPS in 2019, new developments and key publications have been reported, necessitating an update. METHODS: Evidence-based guidelines on 22 topics in 8 domains were proposed: terminology, indications, patients, procedures, surgical techniques and instrumentation, assessment tools, implementation and training, and artificial intelligence. The Brescia Internationally Validated European Guidelines on Minimally Invasive Pancreatic Surgery (EGUMIPS, September 2022) used the Scottish Intercollegiate Guidelines Network (SIGN) methodology to assess the evidence and develop guideline recommendations, the Delphi method to establish consensus on the recommendations among the Expert Committee, and the AGREE II-GRS tool for guideline quality assessment and external validation by a Validation Committee. RESULTS: Overall, 27 European experts, 6 international experts, 22 international Validation Committee members, 11 Jury Committee members, 18 Research Committee members, and 121 registered attendees of the 2-day meeting were involved in the development and validation of the guidelines. In total, 98 recommendations were developed, including 33 on laparoscopic, 34 on robotic, and 31 on general MIPS, covering 22 topics in 8 domains. Out of 98 recommendations, 97 reached at least 80% consensus among the experts and congress attendees, and all recommendations were externally validated by the Validation Committee. CONCLUSIONS: The EGUMIPS evidence-based guidelines on laparoscopic and robotic MIPS can be applied in current clinical practice to provide guidance to patients, surgeons, policy-makers, and medical societies.

Robotic Versus Laparoscopic Liver Resection in Various Settings: An International Multicenter Propensity Score Matched Study of 10.075 Patients.

OBJECTIVE: To compare the perioperative outcomes of robotic liver surgery (RLS) and laparoscopic liver surgery (LLS) in various settings. BACKGROUND: Clear advantages of RLS over LLS have rarely been demonstrated, and the associated costs of robotic surgery are generally higher than those of laparoscopic surgery. Therefore, the exact role of the robotic approach in minimally invasive liver surgery remains to be defined. METHODS: In this international retrospective cohort study, the outcomes of patients who underwent RLS and LLS for all indications between 2009 and 2021 in 34 hepatobiliary referral centers were compared. Subgroup analyses were performed to compare both approaches across several types of procedures: (1) minor resections in the anterolateral (2, 3, 4b, 5, and 6) or (2) posterosuperior segments (1, 4a, 7, 8), and (3) major resections (≥3 contiguous segments). Propensity score matching was used to mitigate the influence of selection bias. The primary outcome was textbook outcome in liver surgery (TOLS), previously defined as the absence of intraoperative incidents ≥grade 2, postoperative bile leak ≥grade B, severe morbidity, readmission, and 90-day or in-hospital mortality with the presence of an R0 resection margin in case of malignancy. The absence of a prolonged length of stay was added to define TOLS+. RESULTS: Among the 10.075 included patients, 1.507 underwent RLS and 8.568 LLS. After propensity score matching, both groups constituted 1.505 patients. RLS was associated with higher rates of TOLS (78.3% vs 71.8%, P < 0.001) and TOLS+ (55% vs 50.4%, P = 0.026), less Pringle usage (39.1% vs 47.1%, P < 0.001), blood loss (100 vs 200 milliliters, P < 0.001), transfusions (4.9% vs 7.9%, P = 0.003), conversions (2.7% vs 8.8%, P < 0.001), overall morbidity (19.3% vs 25.7%, P < 0.001), and microscopically irradical resection margins (10.1% vs. 13.8%, P = 0.015), and shorter operative times (190 vs 210 minutes, P = 0.015). In the subgroups, RLS tended to have higher TOLS rates, compared with LLS, for minor resections in the posterosuperior segments (n = 431 per group, 75.9% vs 71.2%, P = 0.184) and major resections (n = 321 per group, 72.9% vs 67.5%, P = 0.086), although these differences did not reach statistical significance. CONCLUSIONS: While both produce excellent outcomes, RLS might facilitate slightly higher TOLS rates than LLS.

Amiodarone and Dronedarone Causes Liver Injury with Distinctly Different Clinical Presentations.

OBJECTIVE: To describe hepatotoxicity due to amiodarone and dronedarone from the DILIN and the US FDA's surveillance database. METHODS: Hepatotoxicity due to amiodarone and dronedarone enrolled in the U.S. Drug Induced Liver Injury Network (DILIN) from 2004 to 2020 are described. Dronedarone hepatotoxicity cases associated with liver biopsy results were obtained from the FDA Adverse Event Reporting System (FAERS) from 2009 to 2020. RESULTS: Among DILIN's 10 amiodarone and 3 dronedarone DILIN cases, the latency for amiodarone was longer than with dronedarone (388 vs 119 days, p = 0.50) and the median ALT at DILI onset was significantly lower with amiodarone (118 vs 1191 U/L, p = 0.05). Liver biopsies in five amiodarone cases showed fibrosis, steatosis, and numerous Mallory-Denk bodies. Five patients died although only one from liver failure. One patient with dronedarone induced liver injury died of a non-liver related cause. Nine additional cases of DILI due to dronedarone requiring hospitalization were identified in the FAERS database. Three patients developed liver injury within a month of starting the medication. Two developed acute liver failure and underwent urgent liver transplant, one was evaluated for liver transplant but then recovered spontaneously, while one patient with cirrhosis died of liver related causes. CONCLUSION: Amiodarone hepatotoxicity resembles that seen in alcohol related liver injury, with fatty infiltration and inflammation. Dronedarone is less predictable, typically without fat and with a shorter latency of use before presentation. These differences may be explained, in part, by the differing pharmacokinetics of the two drugs leading to different mechanisms of hepatotoxicity.

Safety of robotic cholecystectomy as index training procedure: the UK experience.

AIMS: To evaluate the safety profile of robotic cholecystectomy performed within the United Kingdom (UK) Robotic Hepatopancreatobiliary (HPB) training programme. METHODS: A retrospective evaluation of prospectively collected data from eleven centres participating in the UK Robotic HPB training programme was conducted. All adult patients undergoing robotic cholecystectomy for symptomatic gallstone disease or gallbladder polyp were considered. Bile duct injury, conversion to open procedure, conversion to subtotal cholecystectomy, length of hospital stay, 30-day re-admission, and post-operative complications were the evaluated outcome parameters. RESULTS: A total of 600 patients were included. The median age was 53 (IQR 65-41) years and the majority (72.7%; 436/600) were female. The main indications for robotic cholecystectomy were biliary colic (55.5%, 333/600), cholecystitis (18.8%, 113/600), gallbladder polyps (7.7%, 46/600), and pancreatitis (6.2%, 37/600). The median length of stay was 0 (IQR 0-1) days. Of the included patients, 88.5% (531/600) were discharged on the day of procedure with 30-day re-admission rate of 5.5% (33/600). There were no bile duct injuries and the rate of conversion to open was 0.8% (5/600) with subtotal cholecystectomy rate of 0.8% (5/600). CONCLUSION: The current study confirms that robotic cholecystectomy can be safely implemented to routine practice with a low risk of bile duct injury, low bile leak rate, low conversion to open surgery, and low need for subtotal cholecystectomy.

Perspectives on robotic HPB training in the UK: a survey analysis.

BACKGROUND: We Published a step-up approach for robotic training in hepato-pancreato-biliary (HPB) surgery has been previously. The approach was mostly based on personal experience and communications between experts and needed appraisal and validation by the HPB surgical community. At the Great Britain and Ireland HPB Association (GBIHPBA) robotic HPB meeting held in Coventry, UK in October 2022, the authors sought consensus from the live audience, with an open forum for answering key questions. The aim of this exercise was to appraise the step-up approach, and in turn, lay the foundation for a more substantial UK robotic HPB surgical curriculum. METHODS: The study was conducted using VEVOX online polling platform at the October 2022 GBIHPBA robotic HPB meeting in Coventry, UK. The questionnaire was designed based on a literature search and was externally validated. The data were collated and analysed to assess patterns of response. RESULTS: A median (IQR) of 104 (96-117) responses were generated for each question. 93 consultants and 61 trainees were present Over 90% were in favour of a standardised training pathway. 93.6% were in favour of the proposed step-up approach, with a significant number (67.3%; p < 0.001) considering three levels of case complexity. CONCLUSION: The survey shows a favourable outlook on adopting step-up training in robotic HPB surgery. Ongoing monitoring of progress, clinical outcomes, and collaboration among surgeons and units will bolster this evidence, potentially leading to an official UK robotic HPB curriculum.

Simultaneous portal and hepatic vein embolization is better than portal embolization or ALPPS for hypertrophy of future liver remnant before major hepatectomy: A systematic review and network meta-analysis.

BACKGROUND: Post-hepatectomy liver failure (PHLF) is the Achilles' heel of hepatic resection for colorectal liver metastases. The most commonly used procedure to generate hypertrophy of the functional liver remnant (FLR) is portal vein embolization (PVE), which does not always lead to successful hypertrophy. Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) has been proposed to overcome the limitations of PVE. Liver venous deprivation (LVD), a technique that includes simultaneous portal and hepatic vein embolization, has also been proposed as an alternative to ALPPS. The present study aimed to conduct a systematic review as the first network meta-analysis to compare the efficacy, effectiveness, and safety of the three regenerative techniques. DATA SOURCES: A systematic search for literature was conducted using the electronic databases Embase, PubMed (MEDLINE), Google Scholar and Cochrane. RESULTS: The time to operation was significantly shorter in the ALPPS cohort than in the PVE and LVD cohorts by 27 and 22 days, respectively. Intraoperative parameters of blood loss and the Pringle maneuver demonstrated non-significant differences between the PVE and LVD cohorts. There was evidence of a significantly higher FLR hypertrophy rate in the ALPPS cohort when compared to the PVE cohort, but non-significant differences were observed when compared to the LVD cohort. Notably, the LVD cohort demonstrated a significantly better FLR/body weight (BW) ratio compared to both the ALPPS and PVE cohorts. Both the PVE and LVD cohorts demonstrated significantly lower major morbidity rates compared to the ALPPS cohort. The LVD cohort also demonstrated a significantly lower 90-day mortality rate compared to both the PVE and ALPPS cohorts. CONCLUSIONS: LVD in adequately selected patients may induce adequate and profound FLR hypertrophy before major hepatectomy. Present evidence demonstrated significantly lower major morbidity and mortality rates in the LVD cohort than in the ALPPS and PVE cohorts.

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics.

Value of liver biopsy in the diagnosis of drug-induced liver injury.

BACKGROUND & AIMS: The utility of liver biopsy in diagnosing or staging idiosyncratic drug-induced liver injury (DILI) is unclear. The aim of this study was to determine whether liver histology impacted causality assessment in suspected DILI using a novel simulation model. METHODS: Fifty patients enrolled in the DILI Network (DILIN) who had liver biopsies performed within 60 days of DILI onset were randomly selected. All had standard DILIN consensus causality scoring using a 5-point scale (1=definite, 2=highly likely, 3=probable, 4=possible, 5=unlikely) based on 6-month post-injury data. Three experienced hepatologists independently performed a causality assessment using redacted case records, with the biopsy and selected post-biopsy laboratory data removed. The 3 hepatologists also reviewed the liver histology with a hepatopathologist and then repeated causality assessment for each case. RESULTS: Of the 50 cases, there were 42 high causality DILI cases (1, 2 or 3) and 8 low causality cases (4 and 5). The hepatologists judged that liver biopsy was indicated in 62% of patients; after histology review, biopsy was judged to have been helpful in 70% of patients. Histology review changed the causality score in 68% of patients, with an increase in DILI likelihood in 48% and a decrease in 20%. Biopsy results changed diagnostic certainty from less certain (3 or 4) to highly certain (1, 2 or 5) in 38% of patients. CONCLUSIONS: Liver histologic findings may help clarify the diagnosis of DILI. Histology appears to be particularly helpful in cholestatic or equivocal cases of DILI (possible or probable), shifting assessment toward a greater or lower certainty of a DILI diagnosis. LAY SUMMARY: The utility of liver biopsy in diagnosing or staging idiosyncratic drug-induced liver injury (DILI) is unclear. Herein, we show that, in patients with suspected DILI, a liver biopsy can help physicians diagnose DILI or other causes of liver injury with more certainty.

Garcinia cambogia, Either Alone or in Combination With Green Tea, Causes Moderate to Severe Liver Injury.

BACKGROUND & AIMS: Garcinia cambogia, either alone or with green tea, is commonly promoted for weight loss. Sporadic cases of liver failure from G cambogia have been reported, but its role in liver injury is controversial. METHODS: Among 1418 patients enrolled in the Drug-Induced Liver Injury Network (DILIN) from 2004 to 2018, we identified 22 cases (adjudicated with high confidence) of liver injury from G cambogia either alone (n = 5) or in combination with green tea (n = 16) or Ashwagandha (n = 1). Control groups consisted of 57 patients with liver injury from herbal and dietary supplements (HDS) containing green tea without G cambogia and 103 patients from other HDS. RESULTS: Patients who took G cambogia were between 17 and 54 years, with liver injury arising 13-223 days (median = 51) after the start. One patient died, one required liver transplantation, and 91% were hospitalized. The liver injury was hepatocellular with jaundice. Although the peak values of aminotransferases were significantly higher (2001 ± 1386 U/L) in G cambogia group (P < .018), the median time for improvement in total bilirubin was significantly lower compared with the control groups (10 vs 17 and 13 days; P = .03). The presence of HLA-B∗35:01 allele was significantly higher in the G cambogia containing HDS (55%) compared with patients because of other HDS (19%) (P = .002) and those with acute liver injury from conventional drugs (12%) (P = 2.55 × 10-6). CONCLUSIONS: The liver injury caused by G cambogia and green tea is clinically indistinguishable. The possible association with HLA-B∗35:01 allele suggests an immune-mediated mechanism of injury. CLINICAL TRIALS: gov number: NCT00345930.

Intestinal Host Response to SARS-CoV-2 Infection and COVID-19 Outcomes in Patients With Gastrointestinal Symptoms.

BACKGROUND & AIMS: Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of COVID-19, we investigated intestinal infection with SARS-CoV-2, its effect on pathogenesis, and clinical significance. METHODS: Human intestinal biopsy tissues were obtained from patients with COVID-19 (n = 19) and uninfected control individuals (n = 10) for microscopic examination, cytometry by time of flight analyses, and RNA sequencing. Additionally, disease severity and mortality were examined in patients with and without GI symptoms in 2 large, independent cohorts of hospitalized patients in the United States (N = 634) and Europe (N = 287) using multivariate logistic regressions. RESULTS: COVID-19 case patients and control individuals in the biopsy cohort were comparable for age, sex, rates of hospitalization, and relevant comorbid conditions. SARS-CoV-2 was detected in small intestinal epithelial cells by immunofluorescence staining or electron microscopy in 15 of 17 patients studied. High-dimensional analyses of GI tissues showed low levels of inflammation, including down-regulation of key inflammatory genes including IFNG, CXCL8, CXCL2, and IL1B and reduced frequencies of proinflammatory dendritic cells compared with control individuals. Consistent with these findings, we found a significant reduction in disease severity and mortality in patients presenting with GI symptoms that was independent of sex, age, and comorbid illnesses and despite similar nasopharyngeal SARS-CoV-2 viral loads. Furthermore, there was reduced levels of key inflammatory proteins in circulation in patients with GI symptoms. CONCLUSIONS: These data highlight the absence of a proinflammatory response in the GI tract despite detection of SARS-CoV-2. In parallel, reduced mortality in patients with COVID-19 presenting with GI symptoms was observed. A potential role of the GI tract in attenuating SARS-CoV-2-associated inflammation needs to be further examined.

Liver Transplantation for Acute Liver Injury in Asians Is More Likely Due to Herbal and Dietary Supplements.

Drug-induced liver injury (DILI) due to medications and herbal and dietary supplements (HDSs) is a major cause of acute liver injury leading to liver transplantation (LT). This study used United Network for Organ Sharing LT data to analyze severe HDS-induced acute liver injury in the United States. By convention, patients with acute DILI are listed as "Acute Hepatic Necrosis" (AHN) under the subheading "AHN: Drug Other Specify." All patients waitlisted from 1994 to 2020 were divided into 3 subgroups: "HDS DILI," "Non-HDS DILI," and "AHN: unknown drug." Analyses were performed to identify epidemiologic differences between patients with HDS DILI and non-HDS DILI. A subanalysis was performed for transplanted patients, including longitudinal changes. Of 1875 patients waitlisted for LT, 736 (39.2%) underwent LT. The proportion of Asian patients in the HDS DILI group was significantly higher compared with that in the non-HDS DILI group (17.4% versus 3.8%; P < 0.001). Excluding acetaminophen cases, the proportion of Black patients in the HDS DILI versus non-HDS group was significantly lower (8.7% versus 25.3%; P < 0.001). Waitlisted patients with HDS DILI were significantly older (median age, 38 years for HDS DILI versus 31 years for non-HDS DILI; P = 0.03). Lastly, the number of patients requiring LT due to HDS DILI increased significantly over time with more than 70% of cases occurring in the last 10 years (2010-2020) compared with the prior 15 years (1994-2009; Ptrend  = 0.001). Ethnicity may help in identifying the cause of severe acute DILI, a growing problem as more patients experiment with HDS.

HLA-B*35:01 and Green Tea-Induced Liver Injury.

BACKGROUND AND AIMS: Herbal supplements, and particularly multi-ingredient products, have become increasingly common causes of acute liver injury. Green tea is a frequent component in implicated products, but its role in liver injury is controversial. The aim of this study was to better characterize the clinical features, outcomes, and pathogenesis of green tea-associated liver injury. APPROACH AND RESULTS: Among 1,414 patients enrolled in the U.S. Drug-Induced Liver Injury Network who underwent formal causality assessment, 40 cases (3%) were attributed to green tea, 202 to dietary supplements without green tea, and 1,142 to conventional drugs. The clinical features of green tea cases and representation of human leukocyte antigen (HLA) class I and II alleles in cases and control were analyzed in detail. Patients with green tea-associated liver injury ranged in age from 17 to 69 years (median = 40) and developed symptoms 15-448 days (median = 72) after starting the implicated agent. The liver injury was typically hepatocellular (95%) with marked serum aminotransferase elevations and only modest increases in alkaline phosphatase. Most patients were jaundiced (83%) and symptomatic (88%). The course was judged as severe in 14 patients (35%), necessitating liver transplantation in 3 (8%), but rarely resulting in chronic injury (3%). In three instances, injury recurred upon re-exposure to green tea with similar clinical features, but shorter time to onset. HLA typing revealed a high prevalence of HLA-B*35:01, found in 72% (95% confidence interval [CI], 58-87) of green tea cases, but only 15% (95% CI, 10-20) caused by other supplements and 12% (95% CI, 10-14) attributed to drugs, the latter rate being similar to population controls (11%; 95% CI, 10.5-11.5). CONCLUSIONS: Green tea-related liver injury has distinctive clinical features and close association with HLA-B*35:01, suggesting that it is idiosyncratic and immune mediated.

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.

Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website's content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies' detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.

Ransomware: Analysing the Impact on Windows Active Directory Domain Services.

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Classification of Skin Cancer Lesions Using Explainable Deep Learning.

Skin cancer is among the most prevalent and life-threatening forms of cancer that occur worldwide. Traditional methods of skin cancer detection need an in-depth physical examination by a medical professional, which is time-consuming in some cases. Recently, computer-aided medical diagnostic systems have gained popularity due to their effectiveness and efficiency. These systems can assist dermatologists in the early detection of skin cancer, which can be lifesaving. In this paper, the pre-trained MobileNetV2 and DenseNet201 deep learning models are modified by adding additional convolution layers to effectively detect skin cancer. Specifically, for both models, the modification includes stacking three convolutional layers at the end of both the models. A thorough comparison proves that the modified models show their superiority over the original pre-trained MobileNetV2 and DenseNet201 models. The proposed method can detect both benign and malignant classes. The results indicate that the proposed Modified DenseNet201 model achieves 95.50% accuracy and state-of-the-art performance when compared with other techniques present in the literature. In addition, the sensitivity and specificity of the Modified DenseNet201 model are 93.96% and 97.03%, respectively.

A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering.

The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets-a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients' medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients' medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

Multiple Participants' Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing.

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal's Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.