Comparing SVM and Naive Bayes classifiers for text categorization with Wikitology as knowledge enrichment

The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Na\"ive Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +6.36% when compared with baseline results. Na\"ive Bayes classifier is better choice when external enriching is used through any external knowledge base.Comment: 5 page

Assessment of Clinical Outcomes in Patients Presenting with Transverse Myelitis: A Tertiary Care Experience from a Developing Country

Background: Transverse myelitis (TM) is an inflammatory disorder of spinal cord, characterized by acute or sub-acute dysfunction of spinal cord affecting the motor, sensory, and autonomic systems. It may be idiopathic or related to other diseases. Although some patients recover from TM with minor or no residual problems, others suffer permanent impairments that affect their ability to perform ordinary tasks of daily living. Our objective was to determine the frequency of different clinical outcomes in patients presenting with TM. Methods: It was a prospective cohort clinical study conducted from May 2018 till October 2018. Study was conducted in the Department of Neurology at Jinnah Medical College Hospital (JMCH), Karachi. In total 131 patients of TM were enrolled and treated as per standard protocol, and re-evaluated after eight weeks for assessment of clinical outcomes. Results: The average age of patients was 51.15 ± 6.56 years. Out of 131 cases, 36.6% of patients had full recovery and 63.4% had poor recovery while recurrence occurred in 66.7% cases. Urinary frequency was observed in 12.2% cases and incontinence in 6.9% cases. Conclusion: Acute TM has become transformed with recent developments, especially the advent of the MRI and the discovery of biomarkers

Diagnostic Accuracy of Positron Emission Tomography-Computed Tomography (PET-CT Scan) In Detecting Bone Marrow Involvement in Patients with Diffuse Large B cell Lymphoma

Objective: To evaluate the diagnostic accuracy of positron emission tomography combined with CT scan (PET-CT Scan) in detecting bone marrow involvement in patients with diffuse large B-cell lymphoma, keeping bone marrow biopsy as gold standard. Methodology: From November 2017 to May 2018, a cross sectional validation study was carried out at the Aga Khan University in Karachi Department of Oncology's Section of Clinical Hematology. The study comprised a total of 112 patients who were identified as having diffuse large B cell lymphoma after a lymph node was implicated was histopathologically examined. All patients had a PET-CT scan and bone marrow biopsy technique as part of the staging workup. With bone marrow biopsy acting as the gold standard, the diagnostic efficacy of a PET-CT scan for identifying bone marrow involvement was evaluated. Results: Of 112 patients, there were 71(63.39%) males and 41(36.61%) females. The mean age was 45.09±17.36 years. The mean duration of diagnosis was 17.19±6.02 days. Through biopsy, bone marrow involvement was identified in 40 (35.7%) cases. Through a PET-CT scan, bone marrow involvement was identified in 47 (41.9%) cases. The PET- CT scan in comparison with bone marrow biopsy for detecting bone marrow involvement in patients with DLBCL had a sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of 95%, 87.7%, 80.85%, 96.92% and 90.18% respectively. Conclusion: PET-CT scan can accurately detect bone marrow involvement in patients with DLBCL so it can be used in most patients instead of invasive bone marrow biopsy procedure for staging of DLBCL patients

Smart Surveillance and Detection Framework Using YOLOv3 Algorithm

In this paper, we proposed a method for locating, identifying, and admitting the activities of intrigued, in nearly actual time, from outlines gotten by a ceaseless tide of video information from an observation camera. This article endorses the way to follow, distinguish, and take note of the exercises of captivated in about real-time from follows gotten by a nonstop stream of video information from a reconnaissance camera. The appearance takes input, follows an appeared time space and can provide an activity title based on a single format. We illustrate that YOLO is a viable strategy and comparatively quick for localization within the custom dataset. The findings and analysis of the model will be presented in the following sections. The demonstration collects input outlines after a foreordained interim and can dole out an activity name based on a single outline. We anticipated the activity name for the video stream by combining the discoveries over a period. Because of its benefits, this YOLO strategy is utilized to distinguish action. This method may be used in various settings to tackle real-world problems, such as shopping malls, ATMs, banks, offices, homes, and societies. We have developed a model that detects some ideal human actions

Security analysis of network anomalies mitigation schemes in IoT networks

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed

An anomaly mitigation framework for IoT using fog computing

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates

A DDoS attack mitigation framework for IoT networks using fog computing

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks

The Framework of Car Price Prediction and Damage Detection Technique

In this paper, the research area has always been car price forecasting. We demonstrate that using the proposed object detection method, the type of damage can be categorized into two classes with good accuracy damaged and undamaged. So, when we discovered these issues, we decided to develop a mobile application called Car Price Prediction, which allows users to anticipate the price of a used car. So, we trained the damage identification model using our data using a state-of-the-art image detection method convolutional neural network and evaluated the accuracy on a GPU server and a smartphone

Exploration of Shallow Geothermal Energy Aquifers by Using Electrical Resistivity Survey in Laki Range Jamshoro district Sindh, Pakistan

Geothermal water is increasingly used around the world for its exploitation. Bulk electrical resistivity differences can bring significant information on variation of subsurface geothermal aquifer characteristics. The electrical resistivity survey was carried out in Laki range in lower Indus basin in the study area to explore the subsurface geothermal aquifers. The Schlumberger electrode configuration with range from 2 m to 220 m depth was applied. Three prominent locations of hot springs were selected including Laki Shah Saddar, Lalbagh and Kai hot spring near Sehwan city. After processing resistivity image data, two hot water geothermal aquifers were delineated at Laki Shah Sadder hot springs. The depth of first aquifer was 56 m and its thickness 38 m in the limestones. The depth of second aquifer of 190 m and with thickness of 96 m hosted in limestone. In Lalbagh hot springs two geothermal aquifers were delineated on the basis of apparent resistivity contrast, the depth of first aquifer zone in sandstone was in sandstone 15 m and thickness 12 m, while the depth of second aquifer was 61m and thickness was 35m. In Kai hot springs two hot water geothermal aquifers were delineated. The depth of first geothermal aquifer was 21m and thickness was 18 m and the depth of second aquifer was 105 m and thickness was 61m present in sandstone lithology. Present work demonstrates the capability of electrical resistivity images to study the potential of geothermal energy in shallow aquifers. These outcomes could potentially lead to a number of practical applications, such as the monitoring or the design of shallow geothermal systems