14 results
Search Results
2. Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning.
- Author
-
Roy, Neha Chhabra and Prabhakaran, Sreeleakha
- Subjects
- *
BANKING laws , *FRAUD prevention , *CORRUPTION , *ORGANIZATIONAL behavior , *RISK assessment , *DATA security , *RANDOM forest algorithms , *COMPUTERS , *FOCUS groups , *DATA security failures , *INTERVIEWING , *DEBT , *QUESTIONNAIRES , *ARTIFICIAL intelligence , *LOGISTIC regression analysis , *IDENTITY theft , *SECURITY systems , *FINANCIAL stress , *RESEARCH methodology , *CONCEPTUAL structures , *JOB stress , *ARTIFICIAL neural networks , *MACHINE learning , *ALGORITHMS - Abstract
This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. A video-based real-time adaptive vehicle-counting system for urban roads.
- Author
-
Liu, Fei, Zeng, Zhiyuan, and Jiang, Rong
- Subjects
- *
TRAFFIC flow , *CITY traffic , *ROADS , *COMPUTER vision , *TRAFFIC congestion ,DEVELOPING countries - Abstract
In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
4. Secure and reliable wireless advertising system using intellectual characteristic selection algorithm for smart cities.
- Author
-
Yousra Abdul Alsahib S. Aldeen and Abdulhadi, Haider Mohammed
- Subjects
- *
CELL phone advertising , *SMART cities , *AD hoc computer networks , *ALGORITHMS , *ARTIFICIAL intelligence , *COMPUTERS - Abstract
Smart cities wireless advertising (smart mobile-AD) filed is one of the well-known area of research where smart devices using mobile ad hoc networks (MANET) platform for advertisement and marketing purposes. Wireless advertising through multiple fusion internet of things (IoT) sensors is one of the important field where the sensors combines multiple sensors information and accomplish the control of self-governing intelligent machines for smart cities advertising framework. With many advantages, this field has suffered with data security. In order to tackle security threats, intrusion detection system (IDS) is adopted. However, the existing IDS system are not able to fulfill the security requirements. This paper proposes an intellectual characteristic selection algorithm (ICSA) integrated with normalized intelligent genetic algorithm-based min-max feature selection (NIGA-MFS). The proposed solution designs for wireless advertising system for business/advertising data security and other transactions using independent reconfigurable architecture. This approach supports the wireless advertising portals to manage the data delivery by using 4G standard. The proposed reconfigurable architecture is validated by using applications specific to microcontrollers with multiple fusion IoT sensors. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. India: Intruder Node Detection and Isolation Action in Mobile Ad Hoc Networks Using Feature Optimization and Classification Approach.
- Author
-
Kavitha, T., Geetha, K., and Muthaiah, R.
- Subjects
- *
WIRELESS communications equipment , *ALGORITHMS , *ARTIFICIAL intelligence , *CLUSTER analysis (Statistics) , *COMPUTER networks , *COMPUTERS , *INFORMATION storage & retrieval systems , *INFORMATION technology , *ARTIFICIAL neural networks , *CELL phones , *DATA security - Abstract
Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. Real-Time Call Admission Control for Packet-Switched Networking by Cellular Neural Networks.
- Author
-
Levendovszky, János and Alpár Fancsali
- Subjects
- *
NEURAL computers , *COMPUTERS , *ARTIFICIAL intelligence , *SWITCHING circuits , *ELECTRONIC circuits , *ALGORITHMS - Abstract
In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
7. Intelligence-Augmented Rat Cyborgs in Maze Solving.
- Author
-
Yu, Yipeng, Pan, Gang, Gong, Yongyue, Xu, Kedi, Zheng, Nenggan, Hua, Weidong, Zheng, Xiaoxiang, and Wu, Zhaohui
- Subjects
- *
MAZE tests , *CYBORGS , *ARTIFICIAL intelligence , *PROBLEM solving , *BRAIN-computer interfaces , *TASK performance - Abstract
Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
8. Machine Learning in Medicine.
- Author
-
Deo, Rahul C.
- Subjects
- *
MACHINE learning , *DIGITAL resources in medicine , *ARTIFICIAL intelligence , *COMPUTERS in medicine , *PROGNOSIS , *DATA analysis , *LITERATURE reviews , *ALGORITHMS , *LEARNING strategies , *MEDICINE - Abstract
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
9. Semisupervised Learning of Classifiers: Theory, Algorithm and Their Application to Humane Computer Interaction.
- Author
-
Cohen, Ira, Cozman, Fabio G., Sebe, Nicu, Cirelo, Marcelo C., and Huang, Thomas S.
- Subjects
- *
AUTOMATIC classification , *CLASSIFICATION , *COMPUTERS , *ALGORITHMS , *FACIAL expression , *ARTIFICIAL intelligence - Abstract
Automatic classification is one of the basic tasks required in any pattern recognition and human computer interaction application. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data. We provide a new analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance. We also show that, if the conditions are violated, using unlabeled data can be detrimental to classification performance. We discuss the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks, and propose a new structure learning algorithm that can utilize unlabeled data to improve classification. Finally, we show how the resulting algorithms are successfully employed in two applications related to human-Computer interaction and pattern recognition: facial expression recognition and face detection. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
10. Toward a theory of intelligence
- Author
-
Kugel, Peter
- Subjects
- *
ARTIFICIAL intelligence , *COMPUTERS , *ALGORITHMS ,QUESTIONS & answers - Abstract
In 1950, Turing suggested that intelligent behavior might require “a departure from the completely disciplined behavior involved in computation”, but nothing that a digital computer could not do. In this paper, I want to explore Turing''s suggestion by asking what it is, beyond computation, that intelligence might require, why it might require it and what knowing the answers to the first two questions might do to help us understand artificial and natural intelligence. [Copyright &y& Elsevier]
- Published
- 2004
- Full Text
- View/download PDF
11. Tracking Mobile Units for Dependable Message Delivery.
- Author
-
Murphy, Amy L., Roman, Gruia-Catalin, and Varghese, George
- Subjects
- *
MOBILE computing , *ELECTRONIC data processing , *WIRELESS communications , *COMPUTERS , *ARTIFICIAL intelligence , *ALGORITHMS - Abstract
As computing components get smaller and people become accustomed to having computational power at their disposal at any time, mobile computing is developing as an important research area. One of the fundamental problems in mobility is maintaining connectivity through message passing as the user moves through the network. An approach to this is to have a single home node constantly track the current location of the mobile unit and forward messages to this location. One problem with this approach is that, during the update to the home agent after movement, messages are often dropped, especially in the case of frequent movement. In this paper, we present a new algorithm which uses a home agent, but maintains information regarding a subnet within which the mobile unit must be present. We also present a reliable message delivery algorithm which is superimposed on the region maintenance algorithm. Our strategy is based on ideas from diffusing computations as first proposed by Dijkstra and Schölten. Finally, we present a second algorithm which limits the size of the subnet by keeping only a path from the home node to the mobile unit. [ABSTRACT FROM AUTHOR]
- Published
- 2002
- Full Text
- View/download PDF
12. A Modified Hebbian Algorithm for Analog VLSI Neural Network Implementation.
- Author
-
Wasaki, Hiroyuki, Horio, Yoshihiko, and Nakamura, Shogo
- Subjects
- *
ARTIFICIAL neural networks , *ARTIFICIAL intelligence , *ALGORITHMS , *INTEGRATED circuits , *ELECTRONIC circuits , *COMPUTERS - Abstract
Various studies on learning rules for neural networks have been done. However, most of those do not consider the hardware implementation, which is a great drawback in the LSI implementation of neural networks with learning capability. From such a viewpoint, this paper proposes a self-organizing learning rule by modifying the Hebbian learning rule. This rule can be implemented easily on an analog VLSI chip as an on-chip learning rule. The self-organizing ability of the system is verified by simulation experiments. It is shown from the experiment that the learning speed is improved by a factor of 2 to 3, and it is possible to avoid the sudden termination of the learning and the divergence of the synaptic weights. [ABSTRACT FROM AUTHOR]
- Published
- 1993
13. "The logical categories of learning and communication": reconsidered from a polycontextural point of view: Learning in machines and living systems.
- Author
-
Eberhard von Goldammer and Joachim Paul
- Subjects
- *
LEARNING , *COMPUTERS , *ARTIFICIAL neural networks , *ALGORITHMS , *ARTIFICIAL intelligence - Abstract
Purpose - Bateson's model of classifying different types of learning will be analyzed from a logical and technical point of view. While learning 0 has been realized for chess playing computers, learning I turns out today as the basic concept of artificial neural nets (ANN). All models of ANN are basically (non linear) data filters, which is the idea behind simple and behavioristic input-output models. Design/methodology/approach - The paper will discuss technical systems designed on the concept of learning 0 and I and it will demonstrate that these models do not have an environment, i.e. they are non-cognitive and therefore "non-learning" systems. Findings - Models based on Bateson's category of Learning II differ fundamentally from Learning 0 and I. They cannot be modeled any longer on the basis of classical (mono-contextural) logics. Technical artifacts which belong to this category have to be able to change their algorithms (behavior) by their own effort. Learning II turns out as a process which cannot be described or modeled on a sequential time axis. Learning II as a process belongs to the category of (parallel interwoven) heterarchical-hierarchical process-structures. Originality/value - In order to model this kind of process-structures the polycontextural theory has to be used - a theory which was introduced by the German-American Philosopher and Logician Gotthard Günther (1900-1984) and has been further developed by Rudolf Kaehr and others. [ABSTRACT FROM AUTHOR]
- Published
- 2007
14. Combining Bootstrap Aggregation with Support Vector Regression for Small Blood Pressure Measurement.
- Author
-
Lee, Soojeong, Ahmad, Awais, and Jeon, Gwanggil
- Subjects
- *
ALGORITHMS , *ARTIFICIAL intelligence , *BLOOD pressure , *BLOOD pressure measurement , *COMPUTERS , *NURSES , *PROBABILITY theory , *RESEARCH funding , *SAMPLE size (Statistics) , *DATA analysis , *OSCILLOSCOPES , *WAVE analysis , *REPEATED measures design , *DESCRIPTIVE statistics - Abstract
Blood pressure measurement based on oscillometry is one of the most popular techniques to check a health condition of individual subjects. This paper proposes a support vector using fusion estimator with a bootstrap technique for oscillometric blood pressure (BP) estimation. However, some inherent problems exist with this approach. First, it is not simple to identify the best support vector regression (SVR) estimator, and worthy information might be omitted when selecting one SVR estimator and discarding others. Additionally, our input feature data, acquired from only five BP measurements per subject, represent a very small sample size. This constitutes a critical limitation when utilizing the SVR technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To overcome these challenges, a fusion with an asymptotic approach (based on combining the bootstrap with the SVR technique) is utilized to generate the pseudo features needed to predict the BP values. This ensemble estimator using the SVR technique can learn to effectively mimic the non-linear relations between the input data acquired from the oscillometry and the nurse’s BPs. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.