Accelerating Discoveries in Data Science and Artificial Intelligence II

Music is the way to connect many people with different languages, countries, and cultures. With the help of our faces, we can predict the emotion of it and search the songs according to that playlist. Detecting the emotions by using the web camera or other sources and improving the prediction of the emotion play an important role in it. By using this feature, a person can have the flexibility of selecting the playlist instead of manually searching it. This is the way we can improve the user interactivity. Users who are not comfortable using the facial detection method can use the emojis to select the emotions, and songs can be played accordingly. This paper showcases the different modules regarding the face detection and selection of songs from the dataset according to detected emotion. It is a web-based application which requires the web camera as the main feature.

To prevent fraudulent publishing on the Internet, we use an automated tool using classification techniques based on machine learning. To check fraudulent web-based messaging, various classifiers are applied, and the outcomes of these classifiers are compared to determine the optimum job scam detection model. It aids in the detection of untrue employment messages from an extensive number of seats. Two major classifiers, simple and combined, are taken into consideration for post-detection of fraudulent work. Experiment results show, however, that ensemble graders are the best classifier for detecting frauds on unique graders.

The personal care industry has significant growth during the past few years and has emerged as one of the sectors with enormous future growth potential. The main reasons are purchasing personal grooming products and availability among a larger base of economical independent men and women, frequent relevant product launches, and growing beauty consciousness and awareness, especially among the younger population. The present study attempted to contribute to consumer behavior’s existing knowledge of various personal care product brands. It enables one to recognize which category of personal care products is ideal in terms of purchase decision based on indicators such as demographic factors like age, income, and occupation of consumers. This study investigates how the age factor influences the usage of different brands of personal care products.

This chapter focused on a study to generate Pythagorean and Reciprocal Pythagorean Triples with two of three same legs by using the following Lemma: If (x, y, z) is a Pythagorean triple, then (y z, x z, x y) is a Reciprocal Pythagorean triple, (y z, x z, z2) is a Pythagorean triple and vice versa. Extended this Lemma to generate at most all Pythagorean and Reciprocal Pythagorean n-tuples.

With the rapid growth of energy consumption, acceleration of industrialization and urbanization, and the emission of automobile and industrial exhausts, polluting gases are causing incredible harm to nature and also impacting the health of people. The control and prevention of air pollution become required to protect the environment and human lives. Additionally, the prediction of air pollution may offer reliable data on air pollution by predicting the future concentration of pollutants in the air. These days, concentrating on tackling exceptional ecological issues andundertaking activities to forestall and lessen air contamination has become a fundamental and challenging task. Machine learning is an efficient approach in the field of environmental modelling, which can reliably forecast air pollution in advance. Thise chapter focuses on the proposed study, analyzes and reviews forecasting air pollution using different learning techniques and then suggests a possible solution for future work.

At 303 K, the effect of anionic surfactant (Sodium lauryl sulfate), cationic surfactant (cetrimonium bromide), and neutral surfactant (Triton X-100) on the dissociation equilibria of L-Methionine was investigated at varying strengths (0.0, 0.5, 1.0, 1.5, 2.0, and 2.5%) of SLS, CTAB, and TX-100 liquid containing 0.16 mol dm−3 NaCl. The dissociation constants were calculated with the help of the programming language MINIQUAD75, and the models that will provide the best-fit models were recognized with the use of crystallographic R factor, χ2, skewness, and kurtosis for the purpose of statistical analysis. These dissociation constant values are shifted in micellar media while estimated these values in pure water. In the case of charged and neutral micelles, the discrepancies in values have been attributed to the solvent characteristics of the interfacial and the bulk phases, which include contributions from the electrostatic potential of the micellar surface. The trend of log values of step-wise dissociation constants with medium composition has been characterized by using electrostatic and non-electrostatic forces acting on dissociation equilibria. Further, species abundances, dissociation dynamics, and the influence of requisite factors on dissociation constants are displayed.

Software application development teams utilize the process of continuous integration (CI) and continuous delivery (CD), commonly known as CI/CD, to deliver code changes more regularly and reliably. Continuous Integration/Continuous Delivery incorporates a culture, operating principles, and a set of practices that assist the software development teams to produce a highly valued and good quality software application. CI/CD is a best practice in agile process models such as SCRUM and also supports devops teams that aim to speed up the software delivery. CI/CD enables software development teams to concentrate on satisfying business goals while assuring code quality and software security by automating the integration and delivery process.

If there is a bijective function f : V(G) ∪ E(G) → {1, 2, .. …|V(G)| + |E(G)|} that holds true for all of the edges uv in a graph G = (V,E), then it is said to be magical. Super magic is the term used to describe an edge-magic graph if f(V(G)) = {1, 2, .. …p}. The magic strength of a magic graph G is the minimum of all constants, let’s say m(G), where the minimum is calculated over all such bijections. In this study, magic strength is examined and classes of super edge-magic graphs are explained.

The need to outsource complicated data management systems from local servers is driving data owners to the commercial public cloud platform because of its increased flexibility and cost advantages. But employing plaintext keyword searches for routine data consumption is unhelpful because sensitive information must first be encrypted before it’s outsourced. As a result, a safe service for searching cloud data needs to be created. Because of the sheer volume of data, enabling a search request for numerous keywords and returning the appropriate cloud-based documents is crucial. They must also be listed in order of importance to the research. Searchable encryption frequently places more emphasis on Boolean keyword searches or single-keyword searches than on sorting a search’s outcomes. In this chapter, we highlight the problem of using secure cloud data, and multi-keyword ranked searches with privacy protection can provide an initial solution, in the form of multi-keyword ranked searchable encryption (MRSE).

Style change detection is the task of identifying the writing style changes in a text. In general, the documents contain paragraphs of text and each paragraph contains sentences. The possibility of writing style changes occurs in a document at paragraph level or sentence level. At paragraph level, two consecutive paragraphs are written by two different authors, whereas at sentence level, two consecutive sentences are written by two different authors. In general, authors are following the same style throughout a document while writing the text in a document. According to this, if two sentences or paragraphs are written by two different authors, it means the similarity between these two sentences or paragraphs is very less. In this chapter, we propose an approach by using BERT architecture and similarity measures. The BERT model is used for representing the sentences or paragraphs as vectors. The similarities among these vectors are computed by using similarity measures. Different similarity measures are used in this experiment, and the performance of these measures for style change detection are compared. The experiment was performed on the dataset provided in the PAN competition 2022 task of style change detection. In this task, three different tasks are introduced based on the writing style changes at sentence level or at paragraph level. The accuracy measure is used for presenting the results of three tasks of style change detection. The proposed method obtained the best accuracies for three tasks of style change detection when compared with various approaches of style change detection.

Examinations are the most important aspects in student lives. In order to secure high score, they may adopt different cheating approaches during the examinations. Offline examinations have human invigilators to monitor students, who have their own physical limitations. This study aims to solve the exam’s malpractice related issues through computer vision. Computer vision is a subfield of artificial intelligence and machine learning, computer vision becomes one of the hottest fields with its extensive variety of applications and to imitate the commanding capabilities of human vision. We develop a model using computer vision to detect physical behavior like facial expressions, neck movements, body poses and suspicious activities like exchanging sheets, hiding notes. We will develop a model which can also detect prohibited gadgets during the examination. The proposed method uses You Only Look Once (YOLO) algorithm with residual networks as the back bone architecture to inspect cheating in exams through cameras.

Recent decades have witnessed a surge of interest in the study of influence maximization from various directions. However, the area suffers from a lack of rigorous investigation. This research makes an effort to solve this problem by analyzing scientometric indicators based on publications indexed in the Scopus database between 2005 and 2022, and this study aims to shed light on this topic. According to the findings, the publishing rate in this area is on the rise. Further, the scholarly connections shown by this study are striking. China, the United States, Australia, and Singapore each contributed significantly to the research publication. The majority of influential authors in the field of influence maximization originate from China, and most collaboration has occurred between the people of these nations and those of countries like the United States and India. In terms of relevance and citations, Chen is the field’s preeminent author. The word “social networking,” a relatively new field of study in social network analysis, is the most popular. Using information and diffusion, the way a network disseminates data is clearly displayed, allowing us to find key players. This research aimed to give academics interested in studying impact maximization a bird’s-eye view of the available literature on the topic.

Characteristics of cloud computing, such as adaptability and openness, call into question several long-held beliefs about data forensics and access control. Companies use the cloud for its many advantages, including its vast resources in terms of storage, network, and processing power. Concerns about data security remain a major barrier to the widespread use of the cloud. Therefore, in most data forensics applications, plays an essential role. Multiple cryptographic approaches have been proposed and put into practice to ensure the privacy and integrity of transmitted data. Optimization issues emerge often in engineering domains, including structural design, scheduling, economic dispatch, and portfolio investment. The optimization strategies they employ are inspired by nature. Cuckoo Search (CS), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are only a few of the algorithms developed in recent years. CS is a recently developed meta-heuristic algorithmic rule that has demonstrated indisputable practical performance on a variety of continuous optimization problems. Like the exploration done on optimization in cloud computing, this survey article covers the biggest challenges that many strategies confront. This method helps academics provide better answers to issues with optimizing encryption techniques.

This chapter proposes a framework that examines possible categories of image modalities available for diabetic retinopathy (DR) and recommends the type of image modality that is better to perform DR segmentation and appropriate post-segmentation machine vision analysis for better DR diagnosis. The proposed system consists of pre-processing step, which uses an order statistics filter to eliminate the noise from the acquired retinal images. The pre-processed images will be sent to the Hadoop Image Processing Interface (HIPI) architecture, implemented using the map-reduce framework. Necessary map and reduce algorithms were implemented to measure the mean pixel intensity values for each type of image modality. For comparison, the proposed system analysis employs 50 color fundus photographs (CFP), 30 fluorescein angiography (FA), and 50 optical coherence tomography (OCT). The average pixel intensity values for CFP are close to 0.31, 0.21, and 0.19 for the RGB channel. The FA values obtained were relative to 0.19, 0.08, and 0.04 for the RGB channel. Similarly, the values for OCT were 0.04, 0.04, and 0.04 for the RGB channel. In comparison to FA and OCT, our proposed work has shown that CFP produces better results. With the proposed map and reduce algorithms, any other type of fundus image could be implemented in the future. The methodology we have implemented using the HIPI architecture is not found in previous research findings in DR, which is the primary motivation for carrying out our work.

Every robotic system is designed, constructed, and programmed to do a certain job. The control systems of robots allow the robot’s parts to move and work, and they make sure that a programmed series of actions, including the application of forces, is carried out, even if a part of the robot fails. However, in such systems human beings often take the role of controlling, monitoring, and supervising. Rapid advances in robotics and automation are making cities throughout the globe into experimental hubs where these technologies may be tried out in a variety of contexts. Robotic process automation may improve customer service and guarantee human resource procedures are followed to the letter of the law. Robotic process automation is a software technology that controls software robots to mimic human behaviors on digital platforms. Robotic process automation is a technology with the potential to replace human labor by doing repetitive tasks. However, it is widely agreed that robots cannot take the role of human resources professionals and that they are best used to supplement human-driven workflows. Collaborative learning occurs when a team of robots shares and discusses their experiences and strategies. The study’s goal is to demonstrate the superiority of robotic process automation over human labor in the human resource management system. The goal of this chapter is to give a broad, introductory survey of both classic and cutting-edge theoretical methods and applications in the field of “control systems, automation in humans and robotics.”

The patterns and preferences of consumer buying options went digital throughout the world because of the COVID-19 pandemic crisis. Ecommerce increased their buying options, and their opinions and perceptions also changed. According to Statista, India’s 2021 Internet penetration rate increased to 45% from that of 2020. Of Indian’s 1.4 billion population, Internet users as account for 630 million. The size of the Indian ecommerce market was worth USD 46.2 billion as of 2020. It is expected to grow to USD 188 billion by 2025, and it may increase to USD 350 billion by 2030. Global economic activity after the pandemic has shown a trade slowdown. Buyers have become accustomed to buying products and services online. Instead of buying from brick-and-mortar locations, customers have since preferred to purchase goods and services by using digital platforms like online websites, smartphone apps, etc. Even regularly consumed items like food are now being ordering online. This study examines the digital trading impact on traditional marketing and analyzes how the substitution of conventional selling with ecommerce has shaped the future of the Indian ecommerce sector.

With an Android app, you can spot elderly persons who might fall and trip. Anybody with access to an open source platform can create his or her own applications for the Android operating system. Seniors falling is a problem that is commonly dealt with by families and medical professionals. According to experts, the sixth most common cause of death in the USA is falls. Because 21% of falls require emergency medical assistance and 11% of falls result in fractures, remaining 68% fall without seeking medical attention face the risk of developing serious health issues. With this justification in mind, a senior fall detection and care notification system was developed. The software monitors the patient and alerts the caretaker if anything is out of the ordinary. One of this Android program’s primary benefits is its ability to send alert messages to the carer with the required information. The alert messages include crucial information like their position and driving instructions. Before acting further, the person has the ability to cancel a false alarm. This effort will benefit the elderly. The smartphone application can recognize potential falls and alert family, doctors, and other using a user-friendly interface.

National concern for road safety and human involvement in transportation are becoming increasingly important in everyone’s life. As a result, traffic management system must maintain balance in accordance with maximum road limits. The proposed system is based on a technique known as intervention linear minimum spanning tree (ILMST), which employs a topology with lengths that are proportionally equal. When using dynamic topology, there is packet loss during a change of location or a continuous update in networking via vehicle movement from one location to another. In this manner, each node computes the weighted nodes with a number of partitions in order to provide linear time updates reducing the number of connected edges in graph that are repeated. When size of the repeated graphs that relate the GPS route from the maps is reduced, traffic updates avoid recursion and provide best routes for customers. Traffic congestion overhead can be reduced by implementing the proposed methodology. It is possible to avoid it where there are traffic signals and all other sensor-based wireless devices in a vehicular ad hoc network (VANET). The safety measures are also a necessary step based on the communications in routing and other protocols. The system, when combined with a neural network-based positioning system (NNPS) with various perceptrons, can maintain vehicle speed and categorize safety threats such as group classification. A solution can be found by repairing the DDoS attack based on the results of the various aspects that provide output for malicious monitoring.

An AR + ML (augmented reality and machine learning) prototype for augmented reality and machine learning that enables you to cut and paste items from your environment into an image-editing program. The mobile app, the local server, and the object detection/background removal service are the three distinct components that run this prototype. The mobile app and Photoshop are connected through the local server. Using a screen point determines where the camera is pointing on the screen. Presently, a third-party service is in charge of backdrop removal and salience detection. Direct usage of a tool like Deep lap within the mobile app would be much simpler.

High demand for wireless throughput, communication reliability, and user density, in the recent era of wireless connectivity, posts a significant challenge in the research community. To address this, multiple input and multiple output (MIMO) systems emerged as an effective wireless technology, where more users can be accommodated with the diversified huge number of antennas. The vast energy consumption of wireless communication systems has been becoming a daunting task, with an increase in users, to be dealt with precisely. Hence, this paper is an attempt put forth to present a decent study on the impact of the energy efficiency (EE) on the massive MIMO system. It is inferred that the maximal EE is attained when the base station is given several antennas and is configured to operate in “massive MIMO” mode, which lowers the energy cost per user due to multiplexing of many users and array gain from coherent detection reducing interference. In this work, the energy efficiency maximization problem is solved analytically with respect to the pilot reuse factor, number of antennas in the base station, and cell capacity (users). It is also evident from the study that spectral efficiency (SE) gets affected by maximizing energy efficiency. Furthermore, basic principles of the EE-SE trade-off in relation to important system parameters, like the number of antennas in a base station and user equipment, are also explored.

A proposed method for improving cellular networks’ spectrum efficiency is MIMO. Massive MIMO is a group of wireless communication techniques that include multiple input, multiple output, and many paths. It is a unique instance of base station (BS) antenna overprovisioned multiuser MIMO. Coherent beamforming and spectral efficiency are increased by antenna arrays at base stations with hundreds or thousands of active components. The chapter begins with a thorough explanation of massive MIMO followed by critical discussion on important issues like channel estimate, SE, hardware efficiency (HE), and many deployment-related practical concerns. This kind is intended to evaluate the SE realistically, and it is later expanded to take into account the effects of hardware impairments.Wireless communications use multi-cell minimum mean squared error (M-MMSE) technology to reduce multi-cell interference and improve signal quality in cellular networks. MMSE is a linear filter that reduces the channel noise and interference-adjusted mean squared error between broadcast and received signals.

Requirement engineering’s challenges become manageable when applied to the global advancement of programming. There are numerous reasons why something is difficult. Chances could be one of them since the global improvement perspective is more open to gambling. Therefore, it could be one of the main justifications for taking requirement engineering testing seriously. To begin with, it is necessary to identify the factors that genuinely result in these threats. This essay then separates the factors as well as the risks that these elements may bring about. In the context of the global programming improvement viewpoint, an orderly writing survey is completed for the observable evidence of these variables and the risks that may occur during the necessity designing cycle. The list suggests a moderate improvement in aiding exercises in necessity designing in a global programming advancement worldview. This work is very beneficial for those with less experience working in global programming advancement.

Inter-symbol interference is a major impediment to wireless communication that significantly lowers the data quality. The fundamental objective of the equalization techniques is to reconstruct the actual signal using a filter or other ways and eliminate the impact of ISI in order to guarantee the accuracy of data transmission. Channel equalization is one of the techniques through which the effects of ISI can be greatly reduced and in certain cases be eliminated on the received signal. Extensive research has been done on the relevant techniques with mathematical expressions, and the same has been presented under the section Channel Equalization. In this chapter, we explore the three different channel equalization techniques and study how well they perform to reduce the effects of ISI on the received signal. We analyze ZF, MMSE, and MRC equalization techniques and discover that MRC equalization technique offers very low BER rates as compared to ZF and MMSE techniques.

Due to the continuous and variable nature of ad hoc networks, the operations performed by the routing layer have a significant effect on the communication on these networks. Therefore, the most demanding QoS parameters must be implemented in the MANET environment to ensure the satisfactory performance of the network according to the user’s application requirements. So, the routing layer needs to use predictive techniques to react actively to the needs and anticipate the next probable action for stable communication. The quality of the link is reflected in the transmission time of the packet, which depends on the available transmission speed. Poor link quality needs the procedure of complex modulation and coding schemes, resulting in higher channel overhead. Aim of this chapter is to examine the challenges of QoS routing due to path loss, communication errors, and end-to-end delays. Later, it discusses the link state prediction approach and the accuracy of link quality using SNR. It suggests future research directions for improving QoS routing through enhanced link quality prediction.

This chapter deals with the problem of resolving the complicatedness of multifactor authentication (MFA) of any application. In order to validate the user identity during login or other transactions, users must submit various forms of identification, such as passwords, biometric data, or security tokens, according to the multifactor authentication security strategy. MFA seeks to offer a multi-layered barrier of protection that makes it more difficult for unauthorized users to access a target, such as a physical site or an online application. Even if one element is hacked or damaged, the attacker still must get through at least one or more obstacles in order to get access to the target. One issue with MFA is that a user cannot log in to an application if they are unable to access the physical device that is connected to it. If we lose the physical device which is linked to an application, we cannot log into it. So, to overcome it, we need backup codes that are given by the application. The project aims to store the username, password, and backup codes with a highly encrypted algorithm like AES/DES. All this information is stored in a cloud database so that users can login from anywhere.

One of the main causes of death worldwide is traffic accidents. In this chapter, IoT-based accident detection is discussed which provides warning before the situation becomes dangerous and shares the exact location of the vehicle right away. When an accident occurs and the driver is unable to drive, the warning will be sent and their location can be found using GPS (Global Positioning System). This system aids in locating the vehicle with ease, warns the driver when he has consumed alcohol and feels tired, aids in preventing accidents, and expedites the provision of aid in the event of an accident. Because of the sharp rise in vehicle use, there are also more and more risks associated with accident between vehicles. This chapter specifically addresses accidents that happen because of the driver’s negligence. Most of the time, information is not promptly relayed to family members, the ambulance, or the police. As a result, seeking assistance is delayed. Because no one can predict where an accident will occur, we frequently risk being unable to find it. The system introduces a technique for detecting accidents and warning users when they occur. As soon as an accident happens, the vehicle’s technology will identify it and send a message notice with the vehicle’s location and emergency phone numbers. The accident detection system is equipped with temperature sensor, vibration sensor, alcohol sensor, and heart rate sensors.

A cloud storage can be construed as a storage with different devices, diverse domains, and services. It is less expensive and more reliable and efficient when compared to local storage. The cloud storage had one of the significant issue of lack of control in transparency over the data stored. The users cannot assert the compensation if their data is compromised or damaged. The provenance of data can be maintained using the blockchain. In this paper, we study the existing access control policies and real time storage frameworks of blockchain used in applications to solve the existing problems of security and the performance parameters.

This paper presents a survey of different artificial intelligence (AI) applications in the modern electric grid known as smart grid from security perspective and further highlights the importance of Internet of Things (IoT) in smart grid. IoT is used to connect devices in smart grid over the Internet without human intervention for controlling and monitoring the smart grid infrastructure. Large volume of data is generated from these IoT devices in the smart grid. These devices also provide proper load forecasting and data gathering for low cost. IoT devices and data transmitted between them in smart grid is more prone to security attacks. In this paper, different security issues and solutions to them in smart grid using AI techniques are explained in detail. The paper begins with the overview of smart grid communication system architecture followed by the role of IoT in smart grid. Later on, how AI techniques are used to overcome security threats in smart grid is presented comprehensively.

The Proxy Mobile IPv6 protocol is modified using this proposed technique, known as the Augmented Open-Flow Mechanism of PMIPv6 (AU-PMIPv6), to fit the Augmented Open-Flow architecture. To benefit from the Open-Flow strategy, the flexibility dimensions of the PMIPv6 components, such as the Local Mobility Anchor (LMA) and Mobile Access Gateway (MAG), are separated and reconstructed. The location of the mobile node (MN) is maintained by the LMA components, which act as the Open-Flow controller for the switches in the network. The contact access entities that have MAG capabilities communicate with the MN and manage MAG signaling. The two main goals of the suggested strategy are to (a) separate the control and data planes and (b) reduce the handover’s delay.

Security plays a vital role in transmitting confidential information. Cryptographic algorithms are essential in providing data security against malicious attacks. The DNA- based cryptographic algorithm is one of the valuable methods for protection. RSA algorithm is widely used in the popular implementations of Public Key Infrastructures. This model presents a new approach to show how cryptography works in DNA computing. It could also transmit messages securely and effectively using the RSA algorithm with Laplace Transform belonging to public key cryptography. DNA computing technique and Laplace Transform with RSA algorithm to encrypt the message are used in this model. The proposed model shows the frequency, time, and statistical analysis to get good results.

Organizations are placing more and more emphasis on information security. Organizations rely substantially on information technology as digitalization progresses and new technologies are adopted quickly. Information security breaches can have a variety of negative effects on a company’s operations, finances, reputation, and legal standing. At the international level, small and medium organizations account for more than 90% of the business economy. Based on the significant economic contribution that small and medium organizations make, it would be reasonable to assume that they would effectively implement cyber security measures. Attackers are now focusing on small and medium organizations as an easy target because many of them lack the resources or knowledge to secure their networks and information resources. Small and medium organizations continue to be targets of cyberattacks despite the existence of well-known safeguards. In this research paper, we have developed an Integrated Information Security Risk Assessment (IISRA) Framework to implement the appropriate measurement in order to eliminate or minimize the impact that various security related threats and vulnerabilities might have on an organization.

In the past ten years, there has been a lot of research on congestion control and avoidance in wireless sensor networks (WSNs). In addition to managing traffic and resources, mobile nodes can reduce congestion. While using mobile nodes to reduce congestion, such attempts mostly focused on using sink nodes for data collection and avoiding congestion. To support other congestion control algorithms and to deal with congestion in WSNs, this proposed scheme describes two iterations of the Mobile-Congestion Control (M-CC) algorithm in this study. In the first iteration, movable nodes are used to construct detours to the sink that are significant locally. The second iteration makes use of mobile nodes that construct distinct (disjoint) pathways to the sink. The results of the simulation demonstrate that these variations can greatly lessen congestion in WSNs. Other kinds of network problems can also be recovered by using the same method.

The proposed work aims to develop an automated toll collection system for charging vehicles passing through a toll plaza. In India, the existing manual toll collection system is slow and leads to heavy traffic congestion and long queues at toll plazas. This causes significant waiting time, fuel wastage, increased pollution, and reduced highway speeds, negatively affecting the quality of food products, especially perishable items like milk. To address these challenges, an effective solution is presented here, focusing on localizing the license plates of vehicles. This automatic license plate recognition approach utilizes various image processing techniques to read the number plate of a moving car. Initially, the captured image is converted to grayscale to optimize memory consumption and processing speed, by selecting a single plane from the RGB (red, green, blue) channels. A camera captures the image of the moving vehicle from a fixed distance throughout the process. To enhance the image quality, morphological filters are applied, followed by optical character recognition (OCR) using a convolutional neural network (CNN). A database has been created and linked to test the performance of the prototype toll collection system. This database contains information such as the number of vehicles, vehicle owners’ names, unique identification numbers, mobile numbers, and linked bank account balances. The recognized characters from the license plate are compared with the database for matching. Once a match is found, the vehicle proceeds through the toll collection process. This involves an Internet of Things (IoT) integration, where barriers open and close automatically based on the system’s response. The main advantages of this system include fast response times and efficient detection. The results demonstrate a significant reduction in vehicle waiting time, queue length, fuel wastage, and pollution at toll plazas.

Cybercrimes are increasing at a very rapid rate and becoming very heinous on account of the use of growing technological adoption in executing them. The nexus between the criminals and their coordinated network attacks are causing huge damage to the system in very short time. Hence, there is a need for early detection and identification of such offending crime increases. The difficulty in choosing the best strategy, attributing ownership to the collected data for evidence, controlling the device, and authenticating the report generated are issues that need to be addressed because the process of identifying crimes relies on circumstantial evidential data extracted by electrical or physical means. In this paper, a method for detecting deception in suspects and preventing harm through early detection is proposed using a tool called Brain Electrical Oscillations Profiling, which was primarily developed as a forensic tool. Through log monitoring and forensic detection, the actions and methods used during an attack will help create a profile of the perpetrator. Using this knowledge, a new course map for identifying infiltration techniques can be built.