Awais Khan Jumani

Background/Objectives: Alzheimer’s disease (AD) drug development continues to suffer from high late-stage attrition and narrow mechanistic diversity, which makes the systematic repurposing of approved compounds an urgent priority. Existing computational repurposing methods operate on fragmented biomedical knowledge, rarely integrate transcriptomic and pharmacological evidence within a single reasoning process, and provide little of the mechanistic transparency required for clinical translation. This paper presents RepurposeAD, a knowledge graph-guided large language model (LLM) reasoning framework that identifies and ranks repurposed candidates for AD. Methods: RepurposeAD constructs a unified AD-centric knowledge graph from protein interaction networks, drug–target databases, clinical trial registries, and transcriptomic signatures, linking genetic risk loci, amyloid and tau pathway nodes, neuroinflammatory mediators, and established therapeutic mechanisms. A constrained multi-hop path reasoning module scores drug-to-disease routes and attributes each route to one of five pathological axes, after which an LLM-based evidence synthesis module reads the retrieved paths, trial records, and literature to produce calibrated mechanistic justifications and a final iterative re-ranking. Results: On a curated benchmark of 1847 drug–disease associations spanning amyloid, tau, neuroinflammatory, synaptic, and metabolic targets, RepurposeAD attains a top-10 hit rate of 0.83, an area under the precision–recall curve of 0.79, an area under the receiver operating characteristic curve of 0.93, and a mean reciprocal rank of 0.65, exceeding the strongest of five baselines by 0.07 in hit rate and 0.08 in precision–recall area. Three of the ten highest ranked candidates have active or completed trials in AD-adjacent indications. Conclusions: RepurposeAD provides a transparent, scalable, and mechanistically grounded pipeline for accelerating repurposing in AD and related dementias.

Agriculture is one of the vital factors in living, social, and economic stability. The current lifecycle of food safety is multifaceted and unsecured as more stakeholders are involved and sharing resources and related information. This poses serious issues for agriculture industries to provide food safety, transparency, industrial-related information integrity, reliability, trustworthiness in the supply chain, and food livestock against the evolving threats growing in Industry 4.0. This chapter bridges these divergences by enabling a mixture of the “Internet of Things” (IoT) and Blockchain-based novel and secure smart business agricultural transparent supply chain and forecasting architecture. Precision Business Agriculture is a blockchain-IoT-enabled FIWARE cloud-based platform to collect current and previous node transactions and deploy them to the hosts' analytics modules. In this proposed architecture, participating stakeholders create a public ledger (Ethereum) network to share and agree on distinct supply chain and business agriculture forecasting-related activities before preserving the IoT-Blockchain ledger. We have conceived and created smart (digital) contracts, implemented them using pseudo-algorithms, and represented them through sequence diagrams to handle stakeholders' interaction in the supply chain and forecasting process. This proposed solution delivers system integrity, provenance, transparency, preservation, and a robust security mechanism to store immutable agriculture-business-related information in a permissionless hash (SH-256) encrypted IoT-smart contract distributed ledger.

Some of the best sustenance resources come from agriculture, and most nations' economies hinge on it. Technology enacts a vital task in every expertise field. This book part debates the four agribusiness revolutions, each with an associated technology level, focusing on the latest applied science and open issues. Farming can benefit from "information and communication technologies" (ICT) like image processing, cloud/fog support, Blockchain, artificial intelligence (AI), aviation, and the "Internet of Things" (IoT) for a myriad of gains, e.g., meeting farmers' predictions and expectations, accomplishing fungi management, and precluding pest attacks that affect one-third of the planet's annual harvest. What will AI mean for the fourth revolution, Agriculture 4.0 (Agri4)? Agriculturalists are pressured daily to boost their yield by nations demanding it and to furnish proper food worldwide. Farmers already exploit such ICT types in their harvest. A top-grade agribusiness high-tech solution is the agridrone (alias agri-UAV), to safeguard living organisms and soil state, identifying matters from photogrammetry and remote sensing (RS) imageries and making husbandry more impressive and productive without losing farmers' hope in the time crops. This chapter pins down the up-to-the-minute Agri4 applications, emphasizing agridrone technology. Agriculturalists can cut labor with agridrones to protect their crops and diminish fertilizer utilization while easily analyzing the land's imageries. Agridrones can easily follow plants' diseases and stress, delivering possible solutions without damage. Furthermore, they can render digital scenes to visualize crops' health while assessing the ground and creating a 3D field map to inform farmers when planning seed-planting patterns.

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services. Additionally, some players, even after receiving services as per the Service Level Agreement (SLA), claim that they are not receiving services as promised. This poses a significant challenge for cloud service providers in accurately identifying QoE and improving actual services. In this paper, we have compared our previous proposed novel technique that utilizes a deep learning (DL) model to assess QoE through players’ facial expressions during cloud gaming sessions in a virtual reality (VR) environment. The EmotionNET model technique is based on a convolutional neural network (CNN) architecture. Later, we have compared the EmotionNET technique with three other DL techniques, namely ConvoNEXT, EfficientNET, and Vision Transformer (ViT). We trained the EmotionNET, ConvoNEXT, EfficientNET, and ViT model techniques on our custom-developed dataset, achieving 98.9% training accuracy and 87.8% validation accuracy with the EmotionNET model technique. Based on the training and comparison results, it is evident that the EmotionNET model technique predicts and performs better than the other model techniques. At the end, we have compared the EmotionNET results on two network (WiFi and mobile data) datasets. Our findings indicate that facial expressions are strongly correlated with QoE.

This paper investigates virtual reality (VR) technology which can increase the quality of experience (QoE) on the graphics quality within the gaming environment. The graphics quality affects the VR environment and user experience. To gather relevant data, we conduct a live user experience and compare games with high- and low-quality graphics. The qualitative feedback obtained through questionnaires prove the importance ofcontextualizing users’ experiences playing both games. Furthermore, our findings prove the crucial role of graphics quality in adopting user engagement and enjoyment during gaming sessions. Users consistently reported their feeling more connected when interacting with games and receiving high-quality graphics. If the graphics quality received is low, the user rating for a particular game is low. Further examination of VR technology reveals its potential to revolutionize graphics quality within game play.

Natural Language Processing (NLP) is the computerized approach to analyzing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyzer (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown.

Using a feature vector comprising a collection of similarity cues, Content-Based Image Retrieval (CBIR) localizes, obtains, and displays pictures that are similar to those specified in a query. It necessitates data in medical archives and from medical equipment as inputs in order to infer meaning following some processing. An issue that is comparable to the goal image in some way can benefit therapists. CBIR supports text-based retrieval and enhances evidence-based healthcare diagnosis, administration, education, and research. It permits visual/automated diagnosis and smart healthcare decisions in real-time remote screening/consultation/therapy, as well as store-and-forward testing, home care help, and total patient surveillance. Metrics aid in the comparison of visual data and aid in diagnostics. The application scenario can benefit specially built architectures. CBIR requires uniformity of file storage, querying processes, fast picture transmission, realistic databases, worldwide availability, Internet-based structures, and ease of access. This chapter discusses critical and challenging issues of visual content management in healthcare.

Solutions to Intelligent Transportation Systems (ITS) can reduce fatalities enroute to hospitals while allowing health experts to serve people far away. Vehicular Ad Hoc Networks (VANETs) and Flying Ad Hoc Networks (FANETs) fit the bill, particularly in underdeveloped nations. Wireless Sensor Networks (WSNs) in healthcare are increasing, resulting in hitherto unforeseen complications, obstacles, and dilemmas for public e-health administrators. The value of portable health monitoring for older people and remote support in rural areas has increased thanks to legacy mobile systems and smartphones. According to studies, e-health is evolving due to rising technologies such as Web-based, mobile, cloud computing, and the advancement of 5th-era systems. Besides, the WSN reliability and robustness, which might be harmed by interference, are critical for a successful wireless healthcare monitoring deployment. For mobile therapy and medical services, timely and accurate distribution of patient data is crucial. VANETs, for example, is a rapidly expanding healthcare application that is essential to healthcare stakeholders' safety and crowd management. Complexity, unpredictability, dynamic architecture, and high mobility complicate data routing in VANETs and FANETs, crucial for ITSs. The Ad-Hoc On-Demand Distance Vector (AODV) routing protocol is the most prevalent topology-based routing mechanism for VANET. The performance of AODV, on the other hand, decreases as the number of nodes and relative mobility increase.

The Internet of Medical Things (IoMT) designates the interconnection of communication-enabled medical-grade devices and their integration into wider-scale health networks to improve patients’ health. However, because of health-related systems’ critical nature, the IoMT still faces numerous challenges, particularly reliability, safety, and security. This text presents (i) a comprehensive about IoMT improvement through formal methodologies provided by the cyber-physical systems community; (ii) practical applications of medical devices for both patients, staff, caretakers, and healthcare providers; and (iii) unexplored research directions and potential trends to solve uncharted research problems.