In response to the destructiveness caused by terrorists, a framework for pinpointing pivotal nodes within their networks is necessary. This ‎study introduces a Genetic Algorithm-based framework, progressing through three phases to identify crucial nodes. The first phase filters ‎the network, the second employs the robust Genetic Algorithm to pinpoint critical nodes, and the third phase optimises for enhanced ‎accuracy. Empirical results demonstrate the framework's improvement over conventional centrality-based methods, showing enhancements ‎in concurrence, accuracy, and authenticity. The framework proposes a strategic shift toward focusing on the leaders of terrorist networks. ‎This strategic recalibration optimises law enforcement efforts, streamlining their interventions for maximum impact. The inherent potential of ‎this approach resonates in its capacity to significantly enhance the efficiency of security agencies. By concentrating resources on the nodes ‎that truly matter, a more targeted and impactful counter-terrorism strategy can be forged. This innovative framework thus holds the promise ‎of not only more effective counter-terrorism strategies but also a more adept response to the persistent challenges posed by terrorism‎.

Traffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the ‘on’ time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the ‘on’time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions.

The novel coronavirus infection (COVID-19) is not diminishing without vaccine, but it impinges on human safety and economy can be minimized by adopting smart technology to combat pandemic situation. The implementation of new innovations and novel tactics has proven to be effective in curbing the risk of COVID-19. The present study covers the role of smart technology in mitigating the spread of COVID-19 with specific focus on advancement in the field of drone, robotics, artificial intelligence (AI), mask, and sensor technology. The findings shed light on the robotics and drone technology-driven approaches that have been applied for assisting health system, surveillance, and disinfection process, etc. The AI technology strategies and framework is highlighted in terms of bulk data computing, predicting infection threats, providing medical assistance, and analyzing diagnosis results. Besides this, the technological shift in mask and sensor technology during the pandemic have been illustrated, which includes fabrication method like 3D printing and optical sensing, respectively. Furthermore, the strength, weakness, opportunities, and possible threats that have been shaped by the rigorous implementation of these technologies are also covered in detail.

In today’s life social media has special importance in almost everyone’s life and it is being used as a great way to manipulate people’s mind using fake news and fake articles. The topic of fake news came into vision as a serious issue in coming years. To detect and prevent fake news many technologies like blockchain, machine learning, deep learning and natural language processing have been used. This survey paper tells about novel way to compare various technologies of fake news detection and prevention from social media.

Terrorist network may be defined as collection of suspected terrorist nodes which may function in disguise towards accomplishing a terrorist activity. They use extensive communication channel for sharing crucial information. Terrorist network analysis is highly efficacious for intelligence analysis and deriving useful conclusions from available data. Computer Science and Network analysis act as pertinent fields for the study and graphical interpretation of these networks. In this paper, we examine the 26/11 Mumbai attack terrorist network dataset and employ the ELECTRE method for identification of key node in the terrorist network. ELECTRE is an effective multi-criteria decision-making model. It provides a framework for structuring a decision problem integrates the quantitative and qualitative factors of the problem and facilitates easy computation. From the 26/11 Mumbai attack dataset of terrorist network, we have determined that out of several terrorists in the network "Wassi" was the momentous and mastermind of all. The proposed work also demonstrates improvement of result in terms of concurrence, generalization accuracy and genuineness. Based on the solution of ELECTRE framework, it is resolved that the obtained (terrorist) nodes will step up the work of law enforcement agencies and enable them to confine their focus on important members of the terrorist network. Identification of key terrorist is highly important for developing long-term strategies to counter forthcoming terrorist attacks. It can be better implemented during the development of smart city especially for India.

Criminal network investigation is an ignited research area nowadays. There are several types of criminal network. Terrorist network is one of them. Exploring terrorist network is a demand of several investigating agencies. Different properties of network can be better considered to probe network properly using multi criteria decision-making methods. Grey Relational Analysis (GRA) method is a structural technique for organizing and analyzing complex decisions based on typical mathematics. This process can be employed for analyzing available datasets and designing useful conclusions. If network is explored and visualized, it can be destabilized efficiently. Identified members can be studied and thus their future actions can be prevented. These generalized designs can be used by government agencies for destabilization of terrorist group. This system is especially useful for countries like India that face a constant threat from various group of terrorists. This generalization can further be used to destabilize various criminal networks and their activities can be prevented. Thus, life and property of individuals can be saved.

Terrorist network analysis is vital for intelligence analysis and for deriving useful information from available raw data. Computer Science and Graph Theory provide instructive tools for the study and graphical interpretation of these networks. In this paper, we examine the 26/11 Mumbai attack terrorist network dataset and employ the Preference Ranking Organization Method for Enrichment of Evaluation (PROMETHEE) for identification of key node on the terrorist network. PROMETHEE is an effective multi-criteria decision-making model. It provides a framework to find the most suitable alternative by integrating the quantitative and qualitative factors to the decision problem and facilitates easy computation. From the 26/11 Mumbai attacks data set of terrorist network. It is found that out of several terrorists in the network “Wassi” was the focal actor. Based on the PROMETHEE framework, it is resolved that the obtained terrorist nodes can be instrumental for the intelligence and law enforcement agencies to confine their focus on important members of the terrorist network which can deter the functioning of these networks.

The current study attempts to examine the terrorist network. The purpose of this study is to explore the relationship among terrorist nodes and their importance. The study employed a quantitative and qualitative methodology using the power of ORALITE software based on 9/11 terrorist data set. The proposed technique works based on the network parameters like Betweenness, Closeness and PageRank centrality. The FIMAX model consists of four main stages: (a) Computation of Fitness Function. (b) Rank allocation using obtained Fitness Function. (c) Removal of top ranked node. (d) Generation of hierarchical structure using max heap algorithm. The experimental result shows that the proposed methodology is efficacious in assisting to find important nodes and to further destabilize the network. Findings of this study can be considered valuable to law enforcement agencies and scholars in the field of terrorist mining. Global detective agencies can also use these results for investigating the terrorist network.

Terrorist Data Mining basically means to encounter all the data of terrorism from the huge amount of data. In a more intricate way we all know that terrorist set their foot into any predominant place through railway station, bus stands or airport. Usually to communicate they use their cell phones and network. Now if these areas are well equipped with LAN or WAN, that is the Wi-Fi connections surely these terrorist would avail themselves. Then with the help of data used by terrorists their presence can be spotted easily and their information can be collected. This paper describes a novel work to counter the presence of terrorist at public place in a well-defined manner.

The problem of n-queens (n = 8) is a classical search problem in the field of machine learning. Backtracking is frequently applied to solve such constraint based problems, but it often suffers from exponential growth of computing steps/time as n increases. This paper describes the Rakhya's Method, which is an alternative approach to the method of backtracking. It is capable of finding more than one solutions for all values of n>3 in very few steps. There is a significant reduction in the number of steps, for instance, it takes 1,749,317,724 steps to achieve the first solution of 30-queens problem by backtracking whereas RAKHYA`s Method presents its first solution in 32 steps.

Mobile phones finds wide application in audio video and image communication. We can easily transfer multimedia data from one mobile phone to another in few seconds. The present work highlights an innovative approach of FMS to transfer fragrance from one mobile to another. The proposed system will be having a fragrance assembly in mobile with a control and transfer signal method from sender to receiver.