Efficacy of Discectomy by Fenestration Technique in Lumbar Radicular Pain

Objectives:  To know the efficacy of disc excision by fenestration method for the relief of lumbar radicular pain in patients with prolapsed intervertebral disc. Material and Methods:  This descriptive study was conducted in the department of Neurosurgery of Hayatabad Medical Complex, Peshawar, from October 2008 to September 2010. All those patients were included in whom straight leg raising (SLR) sign was less than 60 degree and prolapsed disc at L4 – 5 or L5 – S1 levels on MRI. Patients with multiple level discs, previous history of spine surgery, central disc, evidence of lumbar stenosis and cauda equina syndrome were excluded from this study. All patients were operated in prone position under general anesthesia.  Results:  One hundred and nine patients were studied. 66 (55%) were male and 59 (45%) were female patients. Age rang was from 19 to 52 years with mean age 34.31 years. The commonest level of involvement was L 4 -L 5 in 67 (61%) followed by L5 – S1 in 42 (89%). Sixty five patients had left sided while forty four had right sided symptoms. Majority of patients presented in Dennis pain scale 4 i.e. 67% (n = 73).  Conclusion:  In selected patients with prolapsed intervertebral disc, surgical treatment provides quick pain relief. Fenestration with disc excision is quite a reasonable method to surgically treat the indicated cases of prolapsed disc. Fenestration offers complete visualization of nerve root and complete removal of the offending disc. This procedure does not need greater expertise, sophisticated instrumentation and techniques

Surgical Outcome of Lumbar Disc Surgery in 250 Patients

Objectives:  To study the outcome of patients with Lumbar Disc Herniation managed surgically by fenestration discectomy and laminectomy. Study design:  Retrospective study. Materials and Methods:  This study was conducted in Neurosurgery Department, Hayatabad Medical Complex, Peshawar from Jan, 2006 to December, 2007 with 6 months follow up. Total numbers of patients were 250. Data was collected with the help of performa containing name, age, sex of patient along-with signs and symptoms, investigation, complications and follow up findings. Results:   We included patients of both gender with age ranging from 18 to 60 years with mean age 39 years including 147 (58.8%) male and 103 (41.2%) female ratio 1.42:1, 250 patients were operated upon for Lumbar Disc Herniation. 06 (2.4%) patients had superficial wound infection, 06 (2.4%)had dural tear with 2 (0.8%) postop CSF leak, 04 (1.6%) patients suffered discitis and 14 (5.6%) patients had reherniation of discs at same operative level. Patients with recurrent disc herniation, Disc herniation with spondylolisthesis, patients below 18 years and above 60 years and patient with Disc Prolapse above L3-4 were excluded from the study. Conclusion:  Lumbar disc surgery is safe and effective procedure in good and experienced surgical hands by which pain and neurological deficit of patients can be reduced and prevented giving them good quality of life. Proper patient selection is imperative in achieving successful outcome. By strict selection criteria we can reduce the complications of this procedure. Key words:  Lumbar Disc Herniation, Discectomy, Laminectomy

A meta-heuristic approach for developing PROAFTN with decision tree

© 2016 IEEE. Machine learning algorithms known for their performance in using historical data and examples to predict and classify unknown instances. Decision tree is an efficient machine learning approach that can use data only without the involvement of decision maker to improve the decision making process. Multi-Criteria Decision Analysis (MCDA)is another paradigm used for data classification. In this paper, we propose a new fuzzy classification method based on MCDA called PROAFTN. To use PROAFTN, a set of parameters need to be established from data. The proposed approach uses data pre-processing and canonical genetic algorithm (GA) for obtaining these parameters from data. The generated models have been applied on popular data selected from several application domain, health, economy, etc. According to our experimental study, the new model performs significantly better than decision trees according in terms of accuracy and the interpretation of the decision rules

Investigating Bias in Facial Analysis Systems: A Systematic Review

© 2013 IEEE. Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis

The effects of water on an on-body monopole diversity antenna pair at 1800MHz

This paper presents the effect of water on a pair of 1.8GHz on-body diversity monopole antennas mounted on the forearm of a sitting male static volunteer. Application of a water layer to the forearm was seen to both reduce efficiency and increase directivity leading to a slight overall increase in gain. Increased gain was shown to increase antenna correlation thereby reducing diversity gain in the antenna pair

A prudent based approach for compromised user credentials detection

© Springer Science+Business Media New York 2018. Compromised user credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting compromised legitimate users’ credentials. This is because cyber-criminals can gain access to legitimate users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the user’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about compromised users’ activities for decision or policy makers as to which user is more critical and requires more consideration as compared to less crucial user based prevalence value

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks

The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening

Data analytics in digital marketing for tracking the effectiveness of campaigns and inform strategy

The purpose of the study is to present a digital marketing data analytics model to analyze campaign efficacy and inform strategy based on website performance, social media metrics, email marketing performance, customer data for targeting and personalization, and customer journey analysis. This model defines campaign success criteria for strategy. A statistical analysis approach was used to analyze the data for the research. Data was gathered through a survey. This study analyzes demographic parameters descriptively using the structural equation model (SEM). From comprehensive surveys, 125 digital media and 115 online shop subjects responded. Sampled were 240 people. According to the findings, social media data, customer journey research, successful advertising, and informed approaches are highly correlated. Compared to the previous study, website performance evaluation does not match the marketing plan's success. The model's results can be used by any company that communicates with clients online. © 2023 by the authors; licensee Growing Science, Canada.https://doi.org/10.5267/j.ijdns.2023.3.0157pubpub