A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks.

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

The electrochemical reaction controlled optical response of cholestrol oxidase (COx) conjugated CdSe/ZnS quantum dots.

This paper reports the enhanced performance of cholesterol oxidase (COx) conjugated CdSe/ZnS quantum dots (QDs) by using water-soluble mercaptoacitic acid (MAA) as linker. The functionalized MAA-CdSe/ZnS QDs conjugated in four different dilutions of cholesterol oxidase significantly affected QDs photoluminescence intensities, which affected the process of charge transfer from QDs to MAA. The conjugation of COx to MAA-QDs in increased dilutions resulted in the regain of PL intensities, which were attributed to the passivation of MAA HOMO/LUMO states. The electrochemical impedance spectroscopy and cyclic voltammetry of the conjugated QDs were performed to get study the charge transfer mechanism. The 1:1000 diluted COx conjugated MAA-CdSe/ZnS QDs was found to have the lowest charge transfer resistance of 228 Ω, the highest diffusion (~ 1.39 × 10-13 cm2/s) and charge transfer rates (~ 4.5 × 10-6 s-1) between the electrode and the redox species. The current study demonstrated the sensitivity of electrochemical and optical based detection on the alkaline.

HoneyDetails: A prototype for ensuring patient's information privacy and thwarting electronic health record threats based on decoys.

Advancements in electronic health record system allow patients to store and selectively share their medical records as needed with doctors. However, privacy concerns represent one of the major threats facing the electronic health record system. For instance, a cybercriminal may use a brute-force attack to authenticate into a patient's account to steal the patient's personal, medical or genetic details. This threat is amplified given that an individual's genetic content is connected to their family, thus leading to security risks for their family members as well. Several cases of patient's data theft have been reported where cybercriminals authenticated into the patient's account, stole the patient's medical data and assumed the identity of the patients. In some cases, the stolen data were used to access the patient's accounts on other platforms and in other cases, to make fraudulent health insurance claims. Several measures have been suggested to address the security issues in electronic health record systems. Nevertheless, we emphasize that current measures proffer security in the short-term. This work studies the feasibility of using a decoy-based system named HoneyDetails in the security of the electronic health record system. HoneyDetails will serve fictitious medical data to the adversary during his hacking attempt to steal the patient's data. However, the adversary will remain oblivious to the deceit due to the realistic structure of the data. Our findings indicate that the proposed system may serve as a potential measure for safeguarding against patient's information theft.

State-of-the-art in artificial neural network applications: A survey.

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and area of focus for researchers. Additionally, the study presents ANN application challenges, contributions, compare performances and critiques methods. The study covers many applications of ANN techniques in various disciplines which include computing, science, engineering, medicine, environmental, agriculture, mining, technology, climate, business, arts, and nanotechnology, etc. The study assesses ANN contributions, compare performances and critiques methods. The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems. Therefore, we proposed feedforward and feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance. Moreover, we recommend that instead of applying a single method, future research can focus on combining ANN models into one network-wide application.

Evaluation of fasting and random plasma glucose for diagnosis of gestational diabetes.

OBJECTIVE: To compare different cut-off values of fasting and random plasma glucose as a screening test for diagnosis of gestational diabetes in comparison to the 50 grams Glucose Challenge Test (GCT). STUDY DESIGN: Comparative, cross-sectional study. PLACE AND DURATION OF STUDY: This study was carried out between July 2006 to September 2007 at Departments of Pathology, Obstetrics/Gynaecology and Medicine, PNS Rahat Hospital, Karachi. METHODOLOGY: A total of 53 pregnant subjects at 24-28 weeks of pregnancy were selected to undergo random and fasting blood sugar level estimation and 50-g GCT. All the subjects later underwent 100-g OGTT as well. The results were evaluated by both "Carpenter and Coustan criteria" and "NDDG criteria". The results of random plasma glucose random [cut-off: > or = 11.1 mmol/L], fasting plasma glucose (cut-off: > 5.3 mmol/L and > 5.1 mmol/L) and plasma glucose results post 50-g GCT (cut-off: > or =7.8 mmol/L and > or = 7.2 mmol/L) were evaluated against 100-g OGTT results through ROC curve analysis. Finally, various diagnostic parameters including sensitivity, specificity, predictive values, likelihood ratios (LR) and efficiency were evaluated. RESULTS: Nineteen subjects were diagnosed to have GDM as per the "Carpenter and Coustan criteria" and 13 met the "NDDG criteria" as per the results of 100-g OGTT. Fasting plasma glucose at was the most efficient investigation at cutoff of 5.1 mmol/L sensitivity=66.66%, specificity=81.25%, PPV=70%, NPV=78.78%, LR+=3.56, LR-=0.41, efficiency=75.47%. At the cut-off value of 5.3 mmol/L, the results had 64% sensitivity, 85.71% specificity, 80% PPV, 72.72% NPV, 4.48 LR+, 0.42 LR-, 75.97% efficiency]. It was followed by plasma glucose post 50-g GCT (53.57% sensitivity at cut-off of > or = 7.2 mmol/L and 54.54% sensitivity at cut-off of > or = 7.8 mmol/L). CONCLUSION: Fasting plasma glucose is a better investigation for the screening of gestational diabetes than plasma glucose post 50-g glucose challenge.