Islamabad, Pakistan – On Wednesday evening, as Pakistan grappled with the aftermath of a wave of missile strikes from India that hit at least six cities, killing 31 people, the country's military spokesperson took to a microphone with a chilling warning. "When Pakistan strikes India, it will come at a time and place of its own choosing," Lieutenant General Ahmed Sharif Chaudhry said in a media briefing. "The whole world will come to know, and its reverberation will be heard everywhere." Two days later, India and Pakistan have moved even closer to the brink of war. On Thursday, May 8, Pakistan accused India of flooding its airspace with kamikaze drones that were brought down over major cities, including Lahore and Karachi.

Pakistan's military says it brought down 25 Indian drones over cities including Karachi and Lahore. India says Pakistan had targeted India and Indian-administered Kashmir with drones and missiles that were shot down. The exchanges are fueling fears of a new phase in the ongoing tensions between the nuclear-armed neighbours.

Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques for the application of computer vision. They have implemented proposed several methods using different color detection approaches but still, there is a gap that can be filled. To address this issue, a color detection method, which is based on a Convolutional Neural Network (CNN), is proposed. Firstly, image segmentation is performed using the edge detection segmentation technique to specify the object and then the segmented object is fed to the Convolutional Neural Network trained to detect the color of an object in different lighting conditions. It is experimentally verified that our method can substantially enhance the robustness of color detection in different lighting conditions, and our method performed better results than existing methods.

The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT systems. Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data. This survey paper aims to provide a comprehensive overview of the existing deep learning approaches employed in network traffic classification specifically tailored for IoT environments. By systematically analyzing and categorizing the latest research contributions in this domain, we explore the strengths and limitations of various deep learning models in handling the unique challenges posed by IoT network traffic. Through this survey, we aim to offer researchers and practitioners valuable insights, identify research gaps, and provide directions for future research to further enhance the effectiveness and efficiency of deep learning-based network traffic classification in IoT.

The project's ultimate goal is to meet the country's need for improved telecommunications. On the second day of the mammoth event at the Expo Centre in Karachi, attendees of the International Defence Exhibition and Seminar 2022 (IDEAS-22) showed great enthusiasm for the'One Network' initiative of the Frontier Works Organization (FWO). In a cutting-edge communication initiative called "One Network," workers in Pakistan's highway tunnels are laying 3,000 kilometers of fiber optic cable underneath. Once the project is finished, it will fulfill all of Pakistan's communication needs. The COO of One Network claims that 2,000 kilometers of fiber optic cable have been deployed under the communication backbone of major highways.

KARACHI: More than 25,000 students from different parts of Sindh participated in the Presidential Initiative for Artificial Intelligence and Computing (PIAIC) grand entrance test 2022 here at the National Stadium Karachi (NSK) on Sunday. The initiative is launched to empower youth through training and providing them financial support to become entrepreneurs. Organised by the Saylani Welfare International Trust (SWIT), the test was witnessed by President Dr Arif Alvi, who also addressed the participants and advised them to work hard and focus on information technology. President Alvi asks youth to avail PTI govt's loans scheme to become entrepreneur "Once you complete your education and training, you would avail thousands of opportunities in this sector across the world," he said in his address before the test. "The government is extending all kinds of support to the IT sector and laws have been made to facilitate the growth of this sector," he said.

Modern aircraft are designed with redundant control effectors to cater for fault tolerance and maneuverability requirements. This leads to an over-actuated aircraft which requires a control allocation scheme to distribute the control commands among effectors. Traditionally, optimization based control allocation schemes are used; however, for nonlinear allocation problems these methods require large computational resources. In this work, a novel ANN based nonlinear control allocation scheme is proposed. To start, a general nonlinear control allocation problem is posed in a different perspective to seek a function which maps desired moments to control effectors. Few important results on stability and performance of nonlinear allocation schemes in general and this ANN based allocation scheme, in particular, are presented. To demonstrate the efficacy of the proposed scheme, it is compared with standard quadratic programming based method for control allocation.

Researchers are using artificial intelligence (AI) to detect behavioural signs of anxiety with more than 90 per cent accuracy, and suggest that AI could have future applications for addressing mental health and wellbeing. Their research is published in the journal Pervasive and Mobile Computing. "In the two years since the onset of COVID-19, and one climate disaster after another, more and more people are experiencing anxiety," says Simon Fraser University visiting professor and social psychologist Gulnaz Anjum. "Our research appears to show that AI could provide a highly reliable measurement for recognizing the signs that someone is anxious." Anjum and collaborators Nida Saddaf Khan and Sayeed Ghani from the Institute of Business Administration in Karachi, Pakistan collected an extensive range of data from adult participants for their Human Activity Recognition (HAR) study.

For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding data privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a federated learning and encryption-based private (FLEP) AI framework that provides two-tier security for data and model parameters in an IIoT environment. We proposed a three-layer encryption method for data security and provided a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlighted several open issues and challenges regarding the FLEP AI framework's realization.