Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features

Human Suspicious Activity Recognition(HSAR)is a critical and active research area in computer vision that relies on artificial intelligence reasoning.Significant advances have been made in this field recently due to important applications such as video surveillance.In video surveillance,humans are monitored through video cameras when doing suspicious activities such as kidnapping,fighting,snatching,and a few more.Although numerous techniques have been introduced in the literature for routine human actions(HAR),very few studies are available for HSAR.This study proposes a deep convolutional neural network(CNN)and optimal featuresbased framework for HSAR in video frames.The framework consists of various stages,including preprocessing video frames,fine-tuning deep models(Darknet 19 and Nasnet mobile)using transfer learning,serial-based feature fusion,feature selection via equilibrium feature optimizer,and neural network classifiers for classification.Fine-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature extraction.Next,features are fused in a serial approach,and then an improved optimization method is proposed to select the best features.The proposed technique was evaluated on two action datasets,Hybrid-KTH01 and HybridKTH02,and achieved an accuracy of 99.8%and 99.7%,respectively.The proposed method exhibited higher precision compared to existing state-ofthe-art approaches.

Colitis cystica profunda of the rectum: An unexpected operative finding

Colitis cystic profunda is a rare entity benign condition of the colon and rectum that can mimic suspicious polyps or malignancy. The commonest sites of affectation are the rectum and the sigmoid colon but it can be unusually widely distributed in the colon. The aetiology of this condition is not fully elucidated and confident diagnosis can only be made on histological features. We hereby describe a patient who presented with significant rectal symptoms and an unexpected finding of a submucosal mucous cyst mimicking a suspicious rectal polyp and highlighted its significance and the review of the literature.

Atypical small acinar proliferation and its significance in pathological reports in modern urological times

Atypical small acinar proliferation is a histopathological diagnosis of unspecified importance in prostate needle-biopsy reports,suggestive but not definitive for cancer.The terminology corresponds to some uncertainty in the biopsy report,as the finding might represent an underlying non-cancerous pathology mimicking cancer or an under-sampled prostate cancer site.Therefore,traditional practice favors an immediate repeat biopsy.However,in modern urological times,the need of urgent repeat biopsy is being challenged by some authors as in the majority of cases,the grade of cancer found in subsequent biopsy is reported to be low or the disease to be non-significant.On the other hand,high risk disease cannot be excluded,whereas no clinical or pathological factors can predict the final outcome.In this review,we discuss the significance of the diagnosis of atypical small acinar proliferation in the biopsy report,commenting on its importance in modern urological practice.

Recognition of suspicious behavior using case-based reasoning

A novel method case-based reasoning was proposed for suspicious behavior recognition. The method is composed of three departs: human behavior decomposition, human behavior case representation and case-based reasoning. The new approach was proposed to decompose behavior into sub-behaviors that are easier to recognize using a saliency-based visual attention model. New representation of behavior was introduced, in which the sub-behavior and the associated time characteristic of sub-behavior were used to represent behavior case. In the process of case-based reasoning, apart from considering the similarity of basic sub-behaviors,order factor was proposed to measure the similarity of a time order among the sub-behaviors and span factor was used to measure the similarity of duration time of each sub-behavior, which makes the similarity calculations more rational and comprehensive.Experimental results show the effectiveness of the proposed method in comparison with other related works and can run in real-time for the recognition of suspicious behaviors.

Breast Core-Needle Biopsy in a Large Tertiary Oncologic Centre—1-Year Experience after the Introduction of the Method

Ultrasound (US)-guided core-needle biopsy (CNB) is currently the procedure of choice for work-up of suspicious breast lesion. It is mainly used for evaluation of suspicious breast lesions categorized as BI-RADS 4 and 5 (Breast Imaging-Reporting and Data System). The conducted study included 56 female patients with detected suspicious breast leasions, and they underwent US-guided CNB during 1-year period with the aim to investigate the value of US-guided CNB of the breast in a tertiary-level large-volume oncological centre setting with respect of indications, technical adequacy and safety. 2 patients who entered the study were previously diagnosed as BIRADS 2, 3 patients as BIRADS 3, 18 patients as BIRADS 4 and 33 patients as BIRADS 5. In 14 patients with BC (breast cancer), both FNA (fine-needle aspiration) and CNB were performed, and the malignancy was accurately diagnosed by cytology in 9 patients, confirmed by subsequent CNB in all of them. ADH (atypical ductal hyperplasia) was initialy diagnosed by FNA in 5 patients, and in 2 of them, BC was initialy missed by FNA, but deteced by CNB. As it is known, the cytology has lower sensitivity for detection of BC than hystology, with false-negative rate ranging from 2.5% to 17.9%. In our material, 18.7% of carcinomas were initialy left undetected by FNAC, and subsequently confirmed by CNB. All confirmed carcinomas were correctly suspected on imaging, and categorized as BI-RADS 4 or 5, while all BI-RADS 2 and 3 findings were confirmed as benign on hystology. False-positive rate of imaging was 8%. An average number of 4 tissue cores (range: 2 - 7) was taken in our experience if good quality of the first 3 core was achieved, and there was no consistent reason to proceed with sampling.

Fuzzy Logic Based UAV Suspicious Behavior Detection

In recent years,unmanned air vehicles(UAVs)are widely used in many military and civilian applications.With the big amount of UAVs operation in air space,the potential security and privacy problems are arising.This can lead to consequent harm for critical infrastructure in the event of these UAVs being used for criminal or terrorist purposes.Therefore,it is crucial to promptly identify the suspicious behaviors from the surrounding UAVs for some important regions.In this paper,a novel fuzzy logic based UAV behavior detection system has been presented to detect the different levels of risky behaviors of the incoming UAVs.The heading velocity and region type are two input indicators proposed for the risk indicator output in the designed fuzzy logic based system.The simulation has shown the effective and feasible of the proposed algorithm in terms of recall and precision of the detection.Especially,the suspicious behavior detection algorithm can provide a recall of 0.89 and a precision of 0.95 for the high risk scenario in the simulation.

DPIF:A Framework for Distinguishing Unintentional Quality Problems From Potential Shilling Attacks

Maliciously manufactured user profiles are often generated in batch for shilling attacks.These profiles may bring in a lot of quality problems but not worthy to be repaired.Since repairing data always be expensive,we need to scrutinize the data and pick out the data that really deserves to be repaired.In this paper,we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks.A two-steps framework named DPIF is proposed for the distinguishment.Based on the framework,the metrics of homology and suspicious degree are proposed.The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles.The suspicious degree can be used to identify potential attacks.The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.

Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling

Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.

Performance analysis of legitimate UAV surveillance system with suspicious relay and anti-surveillance technology

We propose a legal Unmanned Aerial Vehicle(UAV)surveillance system to perform passive surveillance or active jamming while in the presence of a suspicious relay or source,where a UAV works in half-duplex mode and the relay/source deploys artificial noise to prevent monitoring.Three schemes are considered for a Multiple-Input Multiple-Output(MIMO)UAV:surveilling followed by jamming;jamming followed by surveilling;and two-stage surveilling.For each scheme,a closed-form expression of surveilling non-outage probability is derived,and surveilling performance under different system configurations is analyzed.Monte Carlo(MC)simulation validates derivation correctness.

Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

Money Laundering in the Central and Eastern Europe

The purpose of this paper is to provide an economic overview of the costs and benefits of anti-money laundering （AML） rules. After defining and explaining the three stages of money laundering, the paper provides an insight into the volume and development of money laundering activities in the Central and Eastern Europe. It relies on international, comparative studies outlines the impact of AML measures on banks and other financial intermediaries Conditions of reporting suspicious activity and government agencies, which use these reports to identify investigation targets, are also analysed. Moreover, the paper discusses possible reasons for the failure of AML rules to fight against the crimes and collateral damage caused by AML. These figures, which are presented in this scientific research, give an indication of how important the money laundering problem and the level of organized crime are.

Web Threats Detection and Prevention Framework

The rapid advancement in technology and the increased number of web applications with very short turnaround time caused an increased need for protection from vulnerabilities that grew due to decision makers overlooking the need to be protected from attackers or software developers lacking the skills and experience in writing secure code. Structured Query Language (SQL) Injection, cross-site scripting (XSS), Distributed Denial of service (DDos) and suspicious user behaviour are some of the common types of vulnerabilities in web applications by which the attacker can disclose the web application sensitive information such as credit card numbers and other confidential information. This paper proposes a framework for the detection and prevention of web threats (WTDPF) which is based on preventing the attacker from gaining access to confidential data by studying his behavior during the action of attack and taking preventive measures to reduce the risks of the attack and as well reduce the consequences of such malicious action. The framework consists of phases which begin with the input checking phase, signature based action component phase, alert and response phases. Additionally, the framework has a logging functionality to store and keep track of any action taking place and as well preserving information about the attacker IP address, date and time of the attack, type of the attack, and the mechanism the attacker used. Moreover, we provide experimental results for different kinds of attacks, and we illustrate the success of the proposed framework for dealing with and preventing malicious actions.

Diagnostic value of traditional Chinese medical dialectical classification in hashimoto's thyroiditis complicated with suspicious nodules

Objective:To explore the diagnostic value of traditional Chinese medical(TCM)dialectical classification in Hashimoto's thyroiditis complicated with suspicious nodules.Methods:The clinical data of patients with Hashimoto's thyroiditis complicated with thyroid nodules in the Department of Breast and thyroid surgery of Weifang Hospital of traditional Chinese Medicine from January 2018 to December 2019 were collected.The patients were examined by 2 or more experienced TCM doctors,and the four diagnostic data were obtained,and then the relevant syndrome types of the patients were judged according to the data.According to the color Doppler ultrasonographic features of thyroid nodules,the patients who met the indication of fine needle aspiration biopsy of thyroid nodules were selected and underwent fine needle aspiration biopsy of thyroid nodules before operation.To analyze the clinical diagnostic value of that the ultrasonic mode used in this study and thyroid cytopathology Bethesda report system combine dialectical classification of traditional Chinese medicine in Hashimoto's thyroiditis complicated with suspected thyroid nodules.Result:A total of 89 patients with Hashimoto's thyroiditis complicated with thyroid nodules were collected.according to the ultrasonic mode,the difference between different modes was statistically significant(P<0.05).The mode of color ultrasound is also related to the dialectical classification of traditional Chinese medicine.The patients with high malignant risk score are mainly qi depression and phlegm stagnation,phlegm and blood stasis,while those with low score are exuberant liver fire and heart liver yin deficiency.According to the study of different The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)classification,the dialectical classification of patients with higher TBSRTC classification was more inclined to qi depression and phlegm stagnation,phlegm and blood stasis,and there was significant difference between different classification(P<0.05).Conclusion:Qi depression and phlegm obstruction,phlegm and blood stasis have high ultrasound malignant risk score and high TBSRTC classification grade in patients with Hashimoto's thyroiditis complicated with suspected thyroid nodules,which has important clinical diagnostic value.

Test-Data Generation Guided by Static Defect Detection

Software testing is an important technique to assure the quality of software systems, especially high-confidence systems. To automate the process of software testing, many automatic test-data generation techniques have been proposed. To generate effective test data, we propose a test-data generation technique guided by static defect detection in this paper. Using static defect detection analysis, our approach first identifies a set of suspicious statements which are likely to contain faults, then generates test data to cover these suspicious statements by converting the problem of test-data generation to the constraint satisfaction problem. We performed a case study to validate the effectiveness of our approach, and made a simple comparison with another test-data generation on-line tool, JUnit Factory. The results show that, compared with JUnit Factory, our approach generates fewer test data that are competitive on fault detection.