Showing papers by "Kennedy Chinedu Okafor published in 2022"

Predictive Forensic Based—Characterization of Hidden Elements in Criminal Networks Using Baum-Welch Optimization Technique

Abstract: AbstractApplying Artificial intelligence tool in dissecting criminal networks is imperative for forensic criminal investigation and prediction. The first step is to identify the hidden links consisting of criminal states (CS), Active Internal Communications (AIC), time frame of attack (TFoA), and the mapped states (MS). Unfortunately, existing strategies lack the computational capacity for predictive intelligence and do not have immediate cover in existing security architectures. Motivated by this, the contributions of this paper are fourfold. First, the prediction of AIC. Second, we determined HLs new information about entities within the network which include: affiliation of the suspected individual with a network; crime characteristics; criminal trends, and/or plans in the global network. Third, Hidden Markov Model (HMM) is introduced to harness key features of CNs while predicting the probable state and timeframe of occurrence of criminal attacks. It is equally applied in determining the most probable sequence of attack vectors/payloads. The parameters are determined for parametric modeling. Fourth, using Baum-Welch Technique (BWT), the obtained parameters are optimized. The result shows that the Foot Soldiers (FS) are most vulnerable with 90% involvement in criminal attacks; the Commander carried out most strategic (high profile) attacks estimated at 2.2%. The private citizens and properties had the highest attack targets (50.6%); whereas the police and military base had 12.3% and 6.7% respectively. The results show that Boko-Haram carried out the greatest level of attacks at 79.2% while Fulani extremists are responsible for 20.8% of all acts of terrorism in Nigeria from 2010 to date.KeywordsArtificial intelligenceAttack payloadBaum-Welch techniqueCriminal network modelComputational intelligencePredictive analytics

Smart Grid Reliability Computation - A Solution to Ageing Infrastructure in Power Grid Networks

Abstract: Ageing infrastructure in the electricity grid network has been a major impeding factor to the power system growth, expansion, and upgrade. Network reliability has greatly affected the potential transition to a smarter grid design. In this paper, a novel smart grid reliability computation that maps the generation, transmission, and distribution into an automated controller is presented. The system supports hybrid gamma-based energy sources including the transmission and distribution network layers while simplifying complex grid automation. Monte Carlo method and Crammer’s rule are employed for reliability computation. Results of smart grid reliability validation with a designed distributed Cloud network, (DCCN), DCell and BCube are used to test the network reliability. Also, a proof-of-concept setup is developed using light C++ code scripts on the PIC18F4550 8-bit chipset at the monitoring zone for load points reliability housekeeping.

Implementation of a Web Application for Stakeholder Inclusion for Sustainable Waste Management

Abstract: This paper presents Waste Management and Hazard Alert System (WM-HAS) web application as a novel business model supporting all stakeholders in the waste management ecosystem for seamless participation in the waste business. The innovative business model is developed for stakeholder inclusion to address the challenges of sustainable waste management in developing countries. The identified stakeholders include the Waste Generators, Pickers, Collectors, and Recyclers. Although, the informal sector has unofficially joined the waste eco-system to do business (i.e., picking, recycling, etc), there is no cohesion or regard for safety. WM-HAS runs on a regular computer or a mobile device and offers all stakeholders a user-friendly interface for doing business. It also integrates an Eyewitness interface and has an Internet of Things (IoT) report page that connects to IoT nodes located at dumpsites to detect hazardous gases. The scrum-Agile methodology is employed with the Laravel model view controller (MVC) framework for both frontend and backend services. Results show user's interface flexibility for ease of business engagements. The audience at a Stakeholder forum Demo in Nigeria expressed a desire to use the WM-HAS App once it goes live.

WearROBOT: An Energy Conservative Wearable Obstacle Detection Robot With LP Multi-Commodity Graph

Abstract: Human activity wearable obstacle detection for the visually impaired (VI) was developed for routine monitoring and observation of surrounding events. Environmental observation, home surveillance, and assistive supports are now built on wearable devices using inertia-based sensors, such as accelerometers, linear acceleration, and gyroscopes. However, previous assisted living system (ALS) still faces challenges in energy management and resource allocation when performing daily activities, particularly with ambulation. Legacy systems cannot fully improve self-esteem, hence, WearROBOT, which detects rearview obstacles and has an audio feedback system incorporated for voicing out once an obstacle is detected. Linear programing (LP) multi-commodity graph (LMCG) learning model is proposed while coupling the shortest path resource allocation for space diversity linearization. An Infrared sensor problem function that minimizes link utilization is derived. Angle-Intensity analysis (AIA) was carried out on various use case scenarios to enable the user to know the best angle to consider depending on its usage and battery conservation. This work showed how intensity differs at various angles of 5°, 15°, 20°, 35°, and 45°. Also, the reflectivity of different materials and how it affects the battery life are studied. As the wearable robot moves away from the node-obstacle, the LMCG narrow-band sensor node (LMCG-NB-IoT) drops energy significantly. The Low Power WAN (LP-WAN), Bluetooth Low-Energy (BLE) and proposed LMCG-NB-IoT offered 51.28%, 33.33%, and 15.39% respectively. In terms of energy latency, the schemes gave 65.63%, 31.25%, and 3.12% respectively. Similarly, the proposed LMCG-NB-IoT had a 50% battery life profile. Finally, WearROBOT mobility aid minimizes injuries experienced by the visually impaired.

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

Abstract: In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient processcomputation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with powersplitting (PS) and time-switching (TS) controls, the MRASMROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status. Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry. Keywords—Cloud computing; cyber-physical systems; complex robot; computational science; IoT; machine learning